Decentralized content fabric

ABSTRACT

Disclosed are examples of systems, apparatus, devices, computer program products, and methods implementing aspects of a decentralized content fabric. In some implementations, one or more processors are configured to execute a software stack to define a fabric node of a plurality of fabric nodes of an overlay network situated in an application layer differentiated from an internet protocol layer. The defined fabric node is configured to: obtain a request for digital content from a client device; obtain, from one or more of the plurality of fabric nodes, a plurality of content object parts of a content object representing, in the overlay network, at least a portion of the digital content; generate consumable media using: raw data stored in the content object parts, metadata stored in the content object parts, and build instructions stored in the content object parts; and provide the consumable media to the client device. In some instances, the consumable media is further generated using a digital contract stored in a blockchain.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains material,which is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure as it appears in the United States Patent andTrademark Office patent file or records but otherwise reserves allcopyright rights whatsoever.

INCORPORATION BY REFERENCE

An Application Data Sheet is filed concurrently herewith as part of thispatent document. Each patent application that this patent documentclaims benefit of or priority to as identified in the concurrently filedApplication Data Sheet is incorporated by reference herein in itsentirety and for all purposes.

TECHNICAL FIELD

This patent document generally relates to digital content routing over adata network. More specifically, this patent document disclosestechniques for ingesting, managing and distributing digital contentusing a decentralized content fabric providing an overlay networksituated on top of an internet protocol layer.

BACKGROUND

Digital content consumption over the Internet is growing explosively.Many argue that value on the Internet has now shifted from data to userattention. Some projections estimate that over 80% of the traffic overthe Internet in 2021 will be video. Of this, the share of live contentis growing the fastest, up from an estimated 2% in 2013 to 13% by 2021,according to some projections. And, with the mainstream availability anduse of gigantic social media, search, and retail platforms whose profitincentives (driven by advertising) are aligned directly with maximizinguser attention, the crux of value has shifted from data to userattention.

However, against this backdrop, the elemental technology of theInternet, which is open and scalable for web data (not large formcontent), has not evolved significantly. Instead, the“client-server-edge-cache” architecture that emerged on top of the openpacket based routing Internet to serve web data (documents) over 20years ago has been stretched to try to support digital content. It isnot difficult to appreciate the incongruence of this design: after all,digital content is typically orders of magnitude larger than webdocuments and creates massively higher traffic demands on the core ofthe Internet and devours terabytes, petabytes, and zettabytes ofstorage. The explosion of piecemeal digital rights management (DRM) anda potpourri of device formats means large form content is re-versionedinto very many end package formats, and in combination with the edgecaching requirements, has further multiplied the bytes that have to bepushed from a source through the Internet core to keep the edge caches“warm” with popular content and has multiplied the storage requirementsthroughout the entire supply chain. Finally, the “push and cache at theedge” model is simply too slow to keep pace with the continuallychanging content of interactive and live sources.

On the economic side, the existing storage and content deliveryproviders, largely content delivery networks (CDNs) and cloud vendors,have high revenues and some have high profit directly from thisentrenched legacy architecture overheated with the consumer demand forvideo. According to some estimates, 71% of Internet traffic is projectedto be carried by CDNs by 2021 (including cloud CDNs). Over the topInternet companies that have built their own infrastructure or Internetgiants that can ride on their own large networks have successfully movedoff of this economic dependence, but the large majority of contentowners/providers including studios, broadcasters and new independentcontent creators who do not have existing Internet business-scaleinfrastructure to ride on currently use this legacy architecture.

This problem is highlighted in that a packet from a typical stream on astreaming service would traverse the link between the website domainserver and a user's Internet service provider (ISP) millions of timesbecause Internet routing is opaque to the actual content being carried.

BRIEF DESCRIPTION OF THE FIGURES

The included drawings are for illustrative purposes and serve only toprovide examples of possible structures and operations for the disclosedsystems, apparatus, methods and computer program products. Thesedrawings in no way limit any changes in form and detail that may be madeby one skilled in the art without departing from the spirit and scope ofthe disclosed implementations.

In the drawings, Figures (FIGS. 1-30 illustrate examples of somesystems, apparatus, methods and computer program products of aspects ofa decentralized content fabric according to some implementations.

FIG. 1 shows an example of a content fabric architecture.

FIG. 2 shows an example of a bootstrapping method for a new fabric nodeto join a content fabric.

FIG. 3 shows an example of a sequence of events in an instance of thebootstrapping method of FIG. 2.

FIG. 4 shows an example of a method for assigning partitions to fabricnodes.

FIG. 5 shows an example of a method for assigning content object partsto a fabric node.

FIG. 6 shows an example of a method for re-partitioning the overlaynetwork of the content fabric.

FIG. 7 shows an example of a content routing method for retrieving acontent object part.

FIG. 8 shows an example of a first fabric node in a domain acquiring acontent object part from a second fabric node in the same domain inresponse to a client sending a request for the content object part tothe first fabric node, when the first fabric node does not have thecontent object part.

FIG. 9 shows an example of an inter-domain routing tree illustrating thelocation of a content object part using a designated partition level.

FIG. 10 shows an example of a first fabric node in a first domainacquiring a content object part from a second fabric node in a seconddomain outside of the first domain in response to a client sending arequest for the content object part to the first fabric node, when thefirst fabric node does not have the content object part.

FIG. 11 shows an example of a content routing method for publishing acontent object part into the content fabric.

FIG. 12 shows an example of a continuous machine learning (ML) methodfor predicting best performing egress nodes and egress-origin segmentsper client.

FIG. 13 shows an example of split schemes for training, validation, andtesting in a timeframe, where the timeframe of a data set chosen forvalidation is shifted forward by one prediction interval relative to thetimeframe for training.

FIG. 14 shows an example of a content object structure in the contentfabric.

FIG. 15 shows an example of a method for finding content objects byhash.

FIG. 16 shows an example of a method for executing content programsagainst content object parts and metadata.

FIG. 17 shows an example of content object versioning.

FIG. 18 shows an example of a method for verification of metadata in acontent object.

FIG. 19 shows an example of a method for verification of a full contentobject part.

FIG. 20 shows an example of a method for verification of a sub-portionof a content object part.

FIG. 21 shows an example of a method for just-in-time (JIT) transcoding,packaging and transport within the content fabric.

FIG. 22 shows an example of IMF package content in the content fabricafter ingest of the selected content type, implemented with bitcode.

FIG. 23 shows an example of description for an English language versionof a consumer streaming deliverable from an interoperable master format(IMF) package where the package specifies multiple language versions.

FIG. 24 shows an example of metadata stored in an original contentobject created from an IMF source package used to specify content,dimensions and placement of a watermark by bitcode in generating anoutput stream.

FIG. 25 shows an example of a flow of content fabric operationsproviding content security and blockchain control.

FIG. 26 shows an example of a method for implementing secure contentcreation in the content fabric.

FIG. 27 shows an example of a method for implementing secure contentaccess in the content fabric.

FIG. 28 shows an example of a transparent, provable chain of record forcontent.

FIGS. 29 and 30 show examples of content object verification trees.

DETAILED DESCRIPTION

Examples of systems, apparatus, methods and computer program productsaccording to the disclosed implementations are described in thissection. These examples are being provided solely to add context and aidin the understanding of the disclosed implementations. It will thus beapparent to one skilled in the art that implementations may be practicedwithout some or all of these specific details. In other instances,certain operations have not been described in detail to avoidunnecessarily obscuring implementations. Other applications arepossible, such that the following examples should not be taken asdefinitive or limiting either in scope or setting.

In the following detailed description, references are made to theaccompanying drawings, which form a part of the description and in whichare shown, by way of illustration, specific implementations. Althoughthese implementations are described in sufficient detail to enable oneskilled in the art to practice the disclosed implementations, it isunderstood that these examples are not limiting, such that otherimplementations may be used and changes may be made without departingfrom their spirit and scope. For example, the operations of methodsshown and described herein are not necessarily performed in the orderindicated. It should also be understood that the methods may includemore or fewer operations than are indicated. In some implementations,operations described herein as separate operations may be combined.Conversely, what may be described herein as a single operation may beimplemented in multiple operations.

Described herein are examples of systems, apparatus, methods andcomputer program products implementing infrastructure, techniques andother aspects of a decentralized content fabric. In someimplementations, the content fabric is configured for ingesting,managing and distributing media and other types of digital content overa network, such as the Internet, with low latency and consistent highbandwidth so streaming and download of digital content is improved incomparison with conventional content delivery systems and techniques.Using some examples of the disclosed content fabric, media can bedistributed globally, for instance, as professional quality “Internettelevision (TV),” at high efficiency and low cost. In someimplementations, such attributes can be made possible by adecentralized, e.g., “nothing shared” infrastructure of the contentfabric that provides one or more of:

self-scaling via a trustless security model, an open fabric-extendprotocol, and strong incentives allowing content owners, viewers,sponsors and infrastructure suppliers to benefit;

bandwidth and storage efficiency compared with conventional cloud,content delivery network (CDN) and digital asset managementarchitectures through a content-centric storage and distributionstructure;

high speed and low latency for on demand and live content by learningbest possible paths to content and locating fast, eligible servers innear real-time;

programmable output, allowing content to carry its code and just-in-time(JIT) application when serving output versions; and

certifiable, tamper-resistant content where version history includingderivatives is transparent and tamper-resistant and content originatorsare provable.

In some implementations, the content fabric incorporates one or more of:

a decentralized, low latency and efficient content routing system;

an efficient distributed storage model and JIT composition of metadata,raw data and code;

a trustless security model with blockchain controlled content accessthat is achieved with proxy re-encryption of content, and authorizationof content access in the content fabric via smart contract transactionson the blockchain;

content integrity verification and origin traceability implemented witha fast, provable content version history backed by blockchaintransactions; and

a metering and compensation system allowing for pricing incentives tobalance supply and demand while meeting service level targets.

Some implementations of a content fabric incorporate content routingtechniques, an audiovisual pipeline (AV pipe), and contracts.

For instance, an AV pipe can incorporate JIT techniques such as JITtranscoding. The AV pipe can provide fully-pipelined transport withminimal latency. Using such an AV pipe, for instance, when a consumer ata computing device clicks play on a player application implemented onthe consumer's device or on a website to play a movie or other video,content object parts of the movie are fetched via an overlay network,described in greater detail herein, using disclosed content routingtechniques to find the content object parts. In some implementations, acontent object part is the fundamental unit of storage, network transferand caching in the content fabric. When the content object parts areretrieved, applying the disclosed techniques, the content object partscan be transcoded in near real-time to often beat a playback deadlinesuch as 2 seconds. Some of the disclosed techniques can be applied toserve the content object parts to a player application so the consumercan see and hear the movie with minimal-to-no perceivable latency.

As mentioned above, in some implementations, the AV pipe works withcontent routing techniques disclosed herein. For instance, in the caseof live streaming, there is very little time to fetch content objectparts of the stream. Some of the disclosed techniques can be performedto realize a JIT rendering pipeline where content object parts relevantto fulfilling a client request, such as a play command at a consumer'scomputing device, are in place except for actual livestream bytes.

Some of the disclosed techniques pertain to digital contracts, alsoreferred to herein as contracts, and applicable blockchain techniques.In some implementations, a contract as well as different versions of acontent object part associated with the contract can be stored in ablockchain. For instance, version proofs for content object parts can begenerated using a hash tree, and the version proof can be verified whena read operation is performed. In some implementations, the versionproof itself is part of what is stored in a blockchain smart contract.And when there is an update operation, a version proof of an updatedcontent object part, in which the content has changed, can be stored inthe blockchain smart contract. Thus, provability of particular contentcan be provided by the blockchain record for such content.

In some implementations, fabric nodes, often implemented as hostcomputing devices running a software stack, also have a contract. Thesefabric nodes can be thought of as workers of a system implementingaspects of the content fabric. By assigning a contract to a fabric node,there is an ability to record trusted work. So, for example, when afabric node performs JIT transcoding on unencrypted content, performanceof such transcoding operations can be recorded against an associatedfabric node contract.

Some of the disclosed techniques provide access control using ablockchain smart contract, interaction with a security model forre-encryption, and storing versions/hashes of a content object part withthe contract in a blockchain ledger. Additional related techniquesfacilitate how consumers get access to content as well as how aconsumer's data is protected using a crypto-wallet. Thesecontract-related services integrate with the security and blockchainaspects of the content fabric, according to some implementations.

In some implementations, a security model is established to manage howcontent is encrypted and how access control is governed. For instance,such a security model can provide encryption methods and operations aswell as re-encryption methods and operations for serving content to arecipient in a trustless manner.

Some of the disclosed implementations provide an overlay network, whichis implemented primarily with application software, as opposed toconventional networks with operations implemented using hardware orlow-level system software. The term “overlay” is used to describeexamples of the overlay network disclosed herein because aspects andoperations are often implemented in an application layer or other typeof layer situated on top of a conventional Internet Protocol (IP) layer.Thus, when the overlay network is implemented in the application layer,some of the disclosed techniques provide for routing by content, asopposed to conventional techniques for routing by host. Thus, in someimplementations, hardware such as hosts, servers, other types ofcomputing devices, etc. are of little relevance when addressing bycontent. That is, in the application layer, it generally does not matterwhich particular computing device stores the content or some part of thecontent. In some implementations, content-centric routing providesfoundational differences between the disclosed content fabric'simplementation using an overlay network versus conventional IP-basedrouting. For instance, a content representation used by the disclosedcontent fabric can be de-duplicated and served from an original source,in contrast with the duplication of content data accompanyingconventional host-centric IP-based routing schemes.

In some implementations, a translation layer is situated between theapplication layer and a conventional IP network implementingtransmission control protocol/internet protocol (TCP/IP), so thetranslation layer serves as an intermediary. For instance, thetranslation layer can facilitate determining which computing devicestores content, which is being requested at the application layer, andcan facilitate reliably transmitting the content over IP.

FIG. 1 shows an example of a content fabric architecture. In FIG. 1, adecentralized content fabric system 100 provides an AV pipe throughwhich media such as video can be generated. In the example of FIG. 1,there are three main logical layers to implement an overlay network: adata layer 104, a code layer 108, and a contract layer 112. When mediais retrieved through system 100, layers 104, 108 and 112 work togetherin near real-time using one or more software stacks to serve the mediaas a consumable 116 through the overlay network. By incorporatingsoftware stacks, system 100 has both vertical and horizontal dimensions;that is, content is retrieved and composed in a supply chain along ahorizontal direction from data layer 104 through code layer 108 andcontract layer 112 to a player 120 providing an output in the form ofconsumable media 116 at a consumer's computing device, while a softwarestack at a fabric node provides a vertical dimension, as explained ingreater detail herein.

In the example of FIG. 1, data layer 104 stores and manages content suchas large form content. Data layer 104 provides an underlying structurefor content, which can get passed around the overlay network. Data layer104 includes an interconnected network of content object parts, whichhave been stored as data files or other blocks of raw data. As explainedin further detail below, each content object part can be identified by ahash value, the result of a one-way hash operation maintained usingcryptography. In some implementations, a content object part isimmutable once finalized and identified by a hash value that iscalculated across all of the data stored in the content object part.Using a cryptographic hash method, the authenticity of a content objectpart's data can be verified by recalculating the hash. The hash alsoserves as a criterion for data deduplication.

In the example of FIG. 1, media provided as an input to data layer 104is broken into pieces of “raw” data 124, often in the form of binaryaudio, video, and/or images. Another input to data layer 104 is metadata128, which describes how to make an output in the form of consumablemedia 116, and can be in the form of text, key values, etc. Buildinginstructions 132, also referred to herein as build instructions 132, aresituated in code layer 108 and are configured to build consumable media116 from raw data 124 and metadata 128. Build instructions 132 are atype of code such as bitcode to facilitate execution in a secure way.Another type of code appearing in system 100 is contracts 136, situatedin contract layer 112 and often implemented using blockchain anddescribed in greater detail herein.

In implementations such as that illustrated in FIG. 1, a content object,described in greater detail herein, includes content object parts in theform of raw data 124 as well as metadata 128. The content object can bemade dynamic and transactional by build instructions 132 and contracts136. Thus, when a movie or other type of media is assembled from contentobject parts, code to render the movie from the content object parts isimmediately available.

In FIG. 1, fabric logic can be implemented in code layer 108. Thus,content routing operations can be provided by code layer 108, amongother dynamic operations. Code layer 108 is configured to transform anddeliver media for consumption through the AV pipe. In someimplementations, continuous machine learning is integrated to tagcontent and otherwise facilitate operation of code layer 108 bydetermining highest bandwidth, lowest latency paths. In code layer 108,content can be classified, and content objects can be identified. Codelayer 108 can generate consumable media JIT on user request. Code layer108 is programmable to combine raw data, metadata and code for dynamic,personalized output.

In FIG. 1, contract layer 112 controls content access via a blockchainledger, in some implementations. Contract layer 112 can be configured toprotect content, for instance, by re-encrypting accessed content usingtrustless encryption. Contract layer 112 also protects user data using,for instance, a cryptographic wallet. In some implementations, contractlayer 112 can provide content transparently and securely to thirdparties. Also, in some implementations, aggregate user data can becollected with “zero knowledge,” as explained in greater detail herein.In some examples, contract layer 112 can be configured to prove versionsof content cryptographically. Version and access history can be recordedin the ledger. Also, contribution of work by fabric nodes can berecorded and compensated.

In some implementations, a blockchain-based versioning protocol isprovided for media, as explained in greater detail herein. Such acustomization and application of blockchain can facilitate adecentralized store of version histories of the content object parts,since versioning/history can be made part of what blockchain holds. Suchversioning techniques are applicable since many content object partsevolve to have different versions. A new version is typically defined byone or more changes, and such a change is often by reference. Versioningcan occur with a single content object part and across a number ofcontent object parts.

In some implementations, a content object's data is stored in datacontainers referred to herein as content object parts. A content objectcan have a referential object structure to facilitate use in the contentfabric. Any media or any large structured or unstructured data in thecontent fabric like a video stream, a file, a package, a set of files,or a data object, e.g., a software program or game, genomics data,structural or mechanical design object, etc. can be made to have acontent object structure using some of the disclosed techniques. Acontent object structure or structures can be generated as part ofingesting media or any source data.

Different implementations of content routing methods are disclosedherein that facilitate distribution of content object parts as well asfinding the content object parts inside a domain and, in some instances,outside of a domain in near real-time. In some examples, an intra-domainrouting protocol is provided. In some implementations, intra-domainrouting can be integrated with distributed and decentralized hashing. Asexplained in greater detail herein, in some examples, an inter-domainrouting protocol with lightweight routing tables is provided.

In some implementations, machine learning (ML) methods can be used toselect best paths among fabric nodes in the content fabric. ML can beused to identify a particular fabric node for communicating with aconsumer's computing device, as well as be used to identify upstreamfabric nodes to get content object parts from.

Some implementations are applicable to different digital media supplychains, such as over-the-top streaming distribution using single“master” formats, low latency live content distribution, personalizedcontent with dynamic and JIT operations such as watermarking, clipping,validation and automatic metadata tagging, and digital assetmarketplaces for scalable value exchange between content owners,viewers, sponsors and licensees.

In some implementations, the content fabric incentivizes participationto drive performance and efficiency, including: for owners andlicensees, scalable, transparent capabilities for rights management,audience reporting and content commerce; for users, transparency forproviding data for their attention to content; and for infrastructureproviders, automatic compensation (payment and cost offset) forcontributing bandwidth, computation and storage.

In some implementations, the disclosed content fabric incorporates:blockchain decentralized ledgers for large scale consensus ondistributed data, tamper resistant storage, and scalable low costmicrotransactions; large scale ML and deep-learning; and scalablecompute infrastructure with graphics processing units (GPUs), tensorprocessing units (TPUs), and other specialized compute platforms.Additionally, blockchain ledgers and smart contracts, crypto-economics,e.g., combining game theory, decentralized ledgers and classic marketeconomics, make it possible to create large scale pricing systems thatwill incentivize supply, demand, and performance in decentralizedsystems.

In some implementations of the overlay network, a software stackprovides relevant operations, and each fabric node runs the samesoftware stack. A fabric node that runs this stack is also referred toherein as a “content node”. In some implementations, the content fabricenables fabric nodes to communicate with one another to securely storeand serve content using a decentralized framework, meaning no state needbe shared via any centralized entities such as databases, tables,ledgers, etc.

In some implementations, content and metadata may be stored once, andconsumable media is rendered on demand. Internally, the content fabricallows digital content to be stored in an object structure, namely acontent object, which can include content object parts, e.g., raw dataas well as metadata, and code which operates on the raw data andmetadata JIT at serving time to yield consumable media versions. Thecode, such as build instructions, that operates on the raw data andmetadata allows for flexible re-use of media, updates of the codewithout updating a software stack (for scalability), and sandboxing forsecurity and metering.

In some implementations, the content fabric may be “trustless” in thatcontent is encrypted and re-encrypted for authorized receivers withoutthe software stack or fabric node on which a software stack runs havingto access content in plain-text, and without having access to thecontent's encryption keys.

In some implementations, access to content objects in the content fabricincluding create, update (write), and view (read) is mediated bytransactions on an embedded blockchain ledger that runs within asoftware stack. An application interface of the content fabric supportsa blockchain platform virtual machine, such as the Ethereum virtualmachine, and, in turn, blockchain smart contracts. Each operation on acontent object can be implemented as a transaction against a smartcontract for that object, in turn recording the address of theentity/user that requested the operation, the identifier of the contentobject and any details of the transaction.

In some implementations, content operations are programmable. Forinstance, base smart contracts for content objects can have custominsertion points (hooks) that allow for invoking any transaction—creditor debit of an account, event logging, authorization, verificationcheck, etc.—before and after any content operation, providing intrinsiccommerce, rights management, and workflow capabilities.

In some implementations, content versions are provable andtamper-resistant. Content objects can have a version proof, such as aMerkle tree calculation of the object's hashes for fast verification ofthe integrity of the object. The root value of the Merkle proof isrecorded in the blockchain transactions for that content object allowingfor a tamper-resistant record of the version history of the object (“whochanged what when”).

In some implementations of the disclosed content fabric, a contentrouting system locates content object parts throughout the overlaynetwork using an original distributed hash table (DHT) structure andglobal, continuous ML to ensure low latency high bandwidth delivery ofthose content object parts to client devices, also referred to herein asclients. Unlike conventional peer-to-peer networks, some implementationsachieve low latency high bandwidth serving even as the system grows innumber of fabric nodes and number of content objects to giant scale. Bythe same token, some implementations of the disclosed systems areincentivized to grow in direct benefit to maintain this highperformance.

In a DHT, the fabric nodes in the network can be treated as the bucketsin a hash map that spans the entire network. The DHT can be keyed byeach fabric node's ID, and the values are any resource associated withor stored by that fabric node, such as file hashes or keywords. In thisway, the fabric node ID not only serves as identification of the fabricnode, but also as a direct map to a set of values to be located in thenetwork. In some contexts, the crux of the DHT's characteristics is theparticular method the DHT uses to search its network to locate thefabric node ID that can return the desired value in response to anapplication or user request.

In some implementations, each fabric node is identified with a 32-bytenode ID, which also is a node address on the embedded blockchain ledger.In some instances, content object part hashes can be sharded over thefabric nodes using a partitioning method that, with the routing method,is designed to a) locate content object parts on a fabric node with lowlatency consistently, even as the number of fabric nodes and number ofcontent objects in the content fabric grows, and b) not require movingaround content as new fabric nodes and content are added.

In some implementations, the partitioning method has globalconfiguration parameters including: 1) a “level,” which defines thenumber of partitions in the network, 2) a “number of partitions pernode” (np), which defines the number of partitions each fabric nodestores, and 3) a static configuration of the number of copies of apartition (cp). In some instances, each partition is itself identifiedby a partition ID (e.g., 4 bits or one hexadecimal number for everyposition in the partition ID), and content object part hashes are alsorepresented as 32-byte IDs.

Content object parts can be assigned to be stored in a partition bymatching a prefix in their part hash to the partition ID, where thelength of this prefix is controlled by the current partition level ofthe network. Similarly, fabric nodes can be assigned to store and servea set of partitions by matching a prefix in their node ID to allpartition IDs that are within (e.g., less than or equal to) an XORdistance calculation that is equal to the configured number ofpartitions per node, also referred to herein as “numparts.” In someexamples, for calculation of the XOR distance, the 32-bytes that make upthe node ID, the content object part hash, and the partition ID areexpressed as 32 pairs of hexadecimal characters (each hex characterrepresenting 4 bits). When content object parts are retrieved from thenetwork, the routing method locates the fabric nodes that can serve thepartition to which the content object part belongs, and uses the mostfavorable fabric node to serve the content object part. In someimplementations, as the number of fabric nodes in the overlay networkincreases to accommodate more content, the network dynamicallyrepartitions with minimal “reshuffling” of existing content objectparts.

In some implementations, given a number of partitions in a network=p, anumber of partitions per fabric node=np, and a number of copies=nc, thenetwork desirably has (p/np)*nc fabric nodes. For example, for a networkthat has 16 partitions, and maintains 8 partitions per fabric node, and7 copies of each partition, the network has (16/8)*7=14 fabric nodes. Asthose partitions start to fill with new content and as the number offabric nodes in the network increases, some implementations can divideeach partition space into 16 smaller partitions, increasing the numberof partitions from 16 to 256, for example, with each existing fabricnode shedding a portion of the now more specific partition space and newfabric nodes taking on the new partition space. Assuming the part hashesare generally evenly dispersed over the partition space, someimplementations can introduce the new partitions and scale up thenetwork while not having to move any content or renumber fabricnodes/content and still maintain the same redundancy of each partition.

In some implementations, each subsequent level uses the next pair ofhexadecimal characters to identify the partition, and contained contentobject parts, and fabric nodes storing the partition. For example,considering the partition ID:

0f 1a 66 aa 4d 5e 6f 7a ab be cd de of 76 e3 a8 44 98 b4 c5 11 00 34 dd3d 47 a8 91 32 fa 01 12

Level 1—uses “0”

Level 2—uses “0f”

Level 3—uses “0f 1”, etc.

In an example, consider a content object part with a hash starting with0f 1a 66 aa . . . . That is assigned to ‘level 3’ partition ID:

0f1,

and a node ID with a hash starting with:

0f 1a 00 00 . . . .

The content object part may stay on this fabric node as the networkgrows from Level 3 through Level 4 because the XOR distance is 0:

XOR distance (0f1, 0f1)=0

XOR distance (0f 1a, 0f 1a)=0

At level 5, the XOR distance calculation between the node ID andpartition ID yields a value of “6”:

XOR distance (0f 1a 6, 0f 1a 0)=6

Assuming the numparts configuration of the network is 7 or greater (XORdistance of 0 through 6), the partition may stay on this fabric node.

Finally, at level 6, the partition ID and node ID diverge beyond the XORdistance constraint and the partition is shed from this fabric node andassumed by a new fabric node:

XOR (0f 1a 66, 0f 1a 00)>>6

Note that the network grew by 3 orders of magnitude before any changeswere made to where this content is located.

In some implementations, eligible content nodes store desired contentobject parts inside of domains of content nodes that are reachable, forinstance, by layer 2 broadcast for intra-domain content routing. In someimplementations, inter-domain routing methods can be performed to locatecontent object parts on fabric nodes outside of the domain. Thedisclosed approaches can be implemented to integrate with large-scale,continuous ML to select fabric nodes and paths in the overlay network(both within domain and out of domain) that are predicted to serve therequesting client with low latency and high bandwidth, at the client'sbottleneck bandwidth capacity.

In some implementations, the content fabric provides to applications(and, in turn, consumers) two primitives for content publishing andretrieval: GET and PUT. Each primitive takes a content object part hashas its primary argument. The content fabric is then responsible toeither locate and return the content object part (GET) or to publish thecontent object part (PUT) to the appropriate fabric nodes based on thepartitioning method.

“Private” IP networks such as small office LANs, corporate WANs andpublic clouds, include host computing devices that can reach one anothervia layer 2 broadcast. IPv6 multicast is by default available withinthese private networks without special configuration. The content fabriccan be configured to take advantage of this capability to use the nativecapability of the network to learn what fabric nodes within the domainhave a content object part and to retrieve the content object partdirectly from the selected fabric node, for instance, selected by MLscore. Fabric nodes join a set of IPv6 multicast groups where theaddresses are directly computed from the partition IDs the fabric nodeis responsible for, and directly reply to requests for content objectparts they are responsible for.

FIG. 2 shows an example of a bootstrapping method for a new fabric nodeto join a content fabric. FIG. 3 shows an example of a sequence ofevents in an instance of the bootstrapping method of FIG. 2. Someimplementations of the method of FIG. 2 can be broken into stages,namely stage 204, in which a routing tree is acquired, stage 208, inwhich a fabric node's partitions are computed and published, and asubscribe operation to subscribe the fabric node to local multicastgroups for those partitions is performed, and stage 212, in whichcontent object parts can be downloaded.

In the example of FIGS. 2 and 3, a new fabric node 216 generates a nodeID for itself as illustrated by block 304 of FIG. 3, and anauthentication can be performed. Fabric node 216 then sends a requestmessage at 224 of FIG. 2 to any existing fabric node 220 of the contentfabric. In response to the request, at 228, existing fabric node 220returns a list of genesis fabric nodes, referring to fabric nodes thathave been present since the initial state of the overlay network, andthus usually have the richest inter-domain partition knowledge. At 228of FIG. 2, existing fabric node 220 also returns a number of currentnetwork parameters, which can include a current network level, thecurrent numparts per node, a number of copies of each partition(numcopies), and a template for IP multicast group subscription. Anexample of these network parameters received by new fabric node 216 isshown in block 308 of FIG. 3.

In FIG. 2, in response to receiving the network parameters, new fabricnode 216 can send a request 230 for inter-domain routing trees to one ormore genesis nodes 232 through a network layer 236. An example of agenesis node is node 11C37A of FIG. 3. The genesis node or nodes 232 canrespond at 240 of FIG. 2 with the latest versions of inter-domainrouting trees, which can be merged at new fabric node 216 for the mostup-to-date version. An example of merged routing trees downloaded fromgenesis nodes is shown in block 312 of FIG. 3.

In FIG. 2, new fabric node 216 computes the partition IDs that node 216will cover as described in greater detail herein. An example of thecomputed partition IDs is shown in block 316 of FIG. 3. New fabric node216 then publishes, at 244 of FIG. 2, the new fabric node's node ID,public IP address, partition IDs and any other associated details to thegenesis fabric node(s) 232 who update their routing trees at 248 withthe details of new fabric node 216.

In FIG. 2, after 244, new fabric node 216 subscribes at 248 to an IPv6multicast group whose address can be directly computed from thepartition IDs that new fabric node 216 is responsible for. An example ofthe subscribe operation is shown in block 320 of FIG. 3. Once new fabricnode 216 has established its routing tree, new fabric node 216 willstart a background method for acquiring the content object parts the newfabric node is responsible for. For instance, in block 324 of FIG. 3,since new fabric node 216 is now ‘online’ and could receive requests forcontent object parts the new fabric node hasn't yet acquired, new fabricnode 216 can acquire content object parts on demand through GET requestsusing the GET operation described in greater detail herein.

FIG. 4 shows an example of a method for assigning partitions to fabricnodes. At 404, a node ID, for instance, in the form of a 32 byte ID, isassigned to a fabric node. In some implementations, the node ID andassignment can be self-generated by the fabric node. The method proceedsfrom 404 to 408, at which the fabric node retrieves network parametersincluding, in this example, network level and numparts. At 412,following 408, the partition for a given node ID for the fabric node iscomputed, and the computed partition is referred to as the basepartition. For example, the first n hex digits of the node ID, wheren=level, can be extracted as a prefix. The method then proceeds to 416,at which the partition set that the fabric node is responsible for iscalculated. In this example, the partition set is a set of partition IDsof size equal to numparts, including the partition ID for the node IDdetermined above, with the property that the XOR distance between theIDs of any partition in the set and the base partition for the fabricnode is less than or equal to numparts. At 420, a method implementing adeterministic algorithm is performed to create a set of size numparts,where each element has the same bit size as the base partition and isrepresented as illustrated in FIG. 4. And, at 420, an XOR of the basepartition and each of the elements of the set illustrated in FIG. 4 iscomputed to find the remaining elements of the partition set.

FIG. 5 shows an example of a method for assigning content object partsto a fabric node. At 504 and 508, inputs in the form of a content objectpart hash and a node ID, for example, in the form of 32 byte IDs, areprovided. At 512, other inputs in the form of a network level andnumparts, as described above at 408 of FIG. 4, are provided. In thisexample, at 512, numcopies is also provided as an input. At 516,following 512, a partition for the content object part is computed.Also, in this example, a prefix is extracted, where the prefix is thefirst n hex digits of the content object part hash, where n is thenetwork level.

In FIG. 5, also following 512, at 520, a node base partition is computedto extract a prefix as described above at 412 of FIG. 4. At 524,following 516 and 520, an XOR distance is computed between the valuesdetermined at 516 and 520. At 528, it is determined whether the XORdistance is less than numparts. If not, the method proceeds to 532, atwhich it is determined that the content object part does not belong onthe fabric node being considered. Returning to 528, if the XOR distanceis less than numparts, the method continues to 536, at which it isconcluded that the content object part belongs on the fabric node.

FIG. 6 shows an example of a method for re-partitioning the overlaynetwork of the content fabric. In some implementations, using the methodof FIG. 6, partitions assigned to a particular fabric node can beadjusted. At 604, current network parameters including network level,numparts and numcopies are retrieved. At 608, input parameters in theform of at least new network level and new numparts for adjusting thepartitioning are provided to desirably replace the current parametersretrieved at 604. Following 608, operations 412, 416 and 420 of FIG. 4are performed.

In FIG. 6, at 624, following 420, for each content object part stored onthe fabric node, it can be determined at 628 whether the given contentobject part belongs on the particular node, for instance, usingtechniques as described above at operations 516, 520, 524 and 528 ofFIG. 5. Possible outcomes of 628 in FIG. 6 include 532 and 536 of FIG.5. In this example, when it is determined at 532 that the content objectpart no longer belongs on the fabric node, the particular content objectpart can be marked at 640 for eviction, for instance, for lateraddressing during a background cleanup method or during live cacheprocessing on the node.

FIG. 7 shows an example of a content routing method for retrieving acontent object part, and FIG. 8 shows an example of a first fabric nodein a domain acquiring a content object part from a second fabric node inthe same domain in response to a client sending a request for thecontent object part to the first fabric node, when the first fabric nodedoes not have the content object part. In FIGS. 7 and 8, a client 704 isin communication with an egress node 708, which is in communication withother fabric nodes 712 in a target multicast group within the domain ofegress node 708, labeled “Domain B” in FIG. 8.

In FIG. 7, two stages of content routing are illustrated: first,intra-domain routing 714, and then inter-domain routing 715. Both typesof routing are explained in greater detail herein. At 720, client 704sends to egress node 708 a request to GET a content object part,illustrated in box 804 in FIG. 8. In response to this request, egressnode 708 calculates a partition for the requested content object partand determines whether the requested part is local to node 708, that is,in one of the partitions of node 708, as shown in FIG. 7. If egress node708 finds, at 722, the requested content object part stored on a disk orother memory at egress node 708, egress node 708 returns the found partto client 704 at 724.

If egress node 708 does not find the requested content object partlocally, egress node 708 initiates an intra-domain GET at 730, whereegress node 708 transmits a GET message to intra-domain nodes 712 usinga network address for the calculated partition such as a multicastaddress, passing the content object part's part ID and, in someinstances, identifiers of preferred nodes based on the fabric nodes'ML-predicted performance scores. An example of the GET message isillustrated in box 808 of FIG. 8. In FIG. 7, if one or more ofintra-domain nodes 712 find the requested content object part at 732,the node(s) 712 return at 734 the requested part to egress node 708,which can then relay the found part to client 704 at 740. In someimplementations, when two or more of intra-domain nodes 712 have therequested content object part, each node 712 returns a respectivenon-overlapping segment of the content object part. Such can bedesirable to avoid a redundant transmission of data from multiple fabricnodes that respond to a request. Responding fabric nodes can computenon-overlapping segments within the requested content object partaccording to a position derived from data in the multicasted request.

In FIG. 7, when no intra-domain nodes 712 are able to find the requestedcontent object part, inter-domain routing 715 begins. In someimplementations, egress node 708 looks up a narrowest, or most precise,match for the partition in egress node 708's copy of an inter-domainrouting tree. In some implementations, egress node 708 finds the mostspecific match to the target partition that has currently known fabricnodes and sends a request to the best scoring of these fabric nodes,identified in FIG. 7 as best match node 716.

For instance, FIG. 9 shows an example of an inter-domain routing treeillustrating the location of a content object part, ‘0f 1a 66 aa 4d 5e6f 7a ab be cd de of 76 e3 a8 44 98 b4 c5 11 00 34 dd 3d 47 a8 91 32 fa01 12’, using a designated partition level. Using such a routing tree,returning to FIG. 7, an IP address of best match node 716 is retrieved,so egress node 708 can send, at 744, a GET to best match node 716 torequest the content object part.

In some implementations, the inter-domain routing converges on averageto find a domain outside of egress node 708's domain containing thecontent object part within well fewer than log(n) steps and within amaximum of log(n) steps (where n is the number of domains in thenetwork), and once the domain is found, the intra-domain lookup isperformed in the external domain to return the content object partimmediately. In some implementations, the inter-domain routing protocoluses a routing table such as that illustrated in FIG. 9 structured as abinary tree where each level in the tree corresponds to increasinglyspecific “levels” within the partition ID being searched for, and a listof node IDs and their address details that are known to have thatpartition ID.

FIG. 10 shows an example of a first fabric node in a first domainacquiring a content object part from a second fabric node in a seconddomain outside of the first domain in response to a client sending arequest for the content object part to the first fabric node, when thefirst fabric node does not have the content object part. FIG. 10 issimilar to FIG. 8 in some respects, with like reference numeralsindicating like parts. In FIG. 10, inter-domain routing requests aresent from Domain B to Domain A, as illustrated. For instance, an exampleof egress node 708 making an inter-domain GET request 744 is illustratedby box 1004. Returning to FIG. 7, if best match node 716 has therequested content object part, at 745, best match node 716 returns therequested part to egress node 708. If best match node 716 does not havethe requested content object part, at 746, best match node 716 canperform operations 730 and 734 within Domain A to find a fabric nodewithin Domain A that has the requested part. Returning to the example ofFIG. 10, operations 730 and 734 performed by node 716 are illustrated inbox 1008.

If the requested content object part is retrieved in Domain A at 747 ofFIG. 7, target nodes in Domain A send respective non-overlappingsegments of the content object part back through egress node 708 toclient 704 at 752. At this stage, it is desirable for egress node 708 toupdate its inter-domain routing tree of FIG. 9 with node IDs thatresponded with content object parts for the designated partition. Anexample of such an update is illustrated in box 1012 of FIG. 10.

It should also be noted that in instances when no fabric nodes in DomainA of FIG. 10 have the requested content object part, additionalinter-domain routing can be performed between Domain B and other domainssuch as Domains C, D, etc. That is, egress node 708 can send requests tohigh scoring fabric nodes in the next level up, e.g., less specific,that are known to have matching partitions.

In some implementations, PUT-ing a new content object part into theoverlay network uses a similar method as GET-ing a content object part.For instance, when a client makes a request to a fabric node, such as aningress node as described below, to PUT the content object part, theingress node can multicast the content object part on the multicastgroups associated with the ingress node's partition ID, and desirablyenough fabric nodes respond to meet a replication requirement for thenetwork. Else the ingress node consults the inter-domain routing treeand publishes the content object part by again finding the most specificmatching known fabric node(s), which apply intra-domain routing until asufficient number of matching target fabric nodes are returned. The newcontent object part is transmitted to these fabric node(s), and therouting tree is updated with learned node IDs and address details.

FIG. 11 shows an example of a content routing method for publishing acontent object part into the content fabric. In FIG. 11, entitiesparticipating in the publishing include a client 1104, an ingress node1108, intra-domain fabric nodes 1112, that is, in the domain of ingressnode 1108, and a best match node 1116 referring to a best match node inan inter-domain routing tree as described above with reference to FIG.9. In FIG. 11, at 1120, client 1104 issues a PUT of a content objectpart to ingress node 1108. In response to the PUT, ingress node 1108calculates a partition for the content object part and determineswhether the content object part is to be stored locally at ingress node1108. At 1124, ingress node 1108 begins intra-domain routing bytransmitting a PUT message to intra-domain nodes 1112, with an addressfor the partition, passing a part ID and numparts. The part IDidentifies the content object part.

In FIG. 11, at 1126, if one or more intra-domain nodes 1112 can confirmthat the content object part can be stored locally at the one or morenodes 1112, at 1128, the node(s) 1112 communicate(s) back to ingressnode 1108 a confirmation message. At 1130, if the number ofconfirmations is equal to or greater than a desired number of replicasindicated by numparts, then registration is complete, in which caseingress node 1108 can confirm, at 1132, back to client 1104 that thecontent object part has been successfully published. Returning to 1128,if the number of confirmations received back from nodes 1112 is lessthan numparts, the method continues at 1136 where ingress node 1108transmits a PUT to a target node outside of the domain of ingress node1108. In this example, the target node is best match node 1116identified using the inter-domain routing tree.

In FIG. 11, best match node 1116 can perform and repeat intra-domainrouting in the domain of node 1116, at 1138, as described above. Thatis, node 1116 can perform PUTs in the fabric nodes in that domain. If,at 1140, the number of confirmations back from nodes in the domain ofbest match node 1116 plus the number of confirmations at 1128 does notequal or exceed numparts, inter-domain routing to additional differentdomains can be performed. Target nodes that accept and store the contentobject part in response to the PUT return confirmations at 1144 back toingress node 1108, at which point ingress node 1108 can update itsinter-domain routing tree with the node IDs of the target nodes thatresponded for the partition. Then, at 1148, ingress node 1108 confirmsback to client 1104 that the content object part was successfullypublished.

In some implementations, a continuous ML system allows individualclients to learn fabric nodes and paths that yield high performance.

In some implementations, the content fabric provides an overlay networkwhere fabric nodes are equal participants in a full mesh network, andcontent object parts including raw data, metadata and code aredelivered. Thus, it is not required for the overlay network to havedirect knowledge of the underlying Internet topology and routinginfrastructure. When a client makes a request of the content fabric toGET content, the client is directly served by a fabric node, which isreferred to herein as an egress node for the sake of ML. That egressnode either has the content object part or searches to find a fabricnode that can supply the content object part, as described in greaterdetail herein. The supplying fabric node is referred to herein as anorigin node for the sake of ML.

In some implementations, in terms of delivery quality, it can bedesirable to optimize with ML:

-   -   start-up latency: it can be desirable for a client to receive        parts with minimum latency, for instance, meeting the “200        milliseconds” considered instantaneous by human perception, to        start playing/experiencing served content.    -   delivery bandwidth: it can be desirable for clients to receive        streams and downloads from the content fabric such that their        “bottleneck bandwidth” in their connection to the Internet is        the bottleneck in receiving speed.    -   least use of core Internet bandwidth: to minimize cost, it can        be desirable to use the core Internet bandwidth as little as        possible given meeting the client quality targets. Thus, it can        be desirable to “localize” the selection of egress and origin        nodes to avoid crossing the Internet core as much as possible.

Given the overlay network model, in some implementations, two separatedimensions can be independently optimized and in turn “learned”—theselection of an egress node, and the selection of an origin noderelative to an egress, which is called an “egress-origin segment.”Additionally some implementations optimize these features for eachindividual client, meaning that not all clients will prefer the samechoices and that some implementations cannot know a priori a good choicefor a client except to “learn” from appropriate peers.

Some implementations construct a collaborative filtering system in whichindividual clients learn the best egress nodes and the bestegress-origin segments by learning from “like” clients. Specifically,some implementations train a collaborative filtering model where clientslearn from clients in their own regions to select egress nodes and learnfrom all clients to select egress-origin segments.

Thus, in some implementations, each client request is served by: anorigin node with constrained bandwidth and capacity, an egress-originsegment in the overlay comprised of underlying network links that can becongested, an egress node with constrained bandwidth and capacity, and aclient-egress segment which may not be controlled by the system.

As client requests are fulfilled, some implementations record thedelivery bandwidth and time to first byte attributable to theegress-origin segment and attributable to the egress nodes.

Some implementations use the score of the segments and egress node totrain the model. And some implementations use the trained model toidentify best segments to route subsequent requests.

In some implementations, a collaborative filtering method simultaneouslyderives the underlying features and the prediction function andminimizes the cost functions for each in two separate applications ofthe model: one that maximizes the delivery bandwidth, and a second thatminimizes the time to first byte (or time to first segment). Someimplementations apply the collaborative filtering method independentlyto the egress-origin segment measurements and the egress nodemeasurements to predict the future scores for each value (bandwidth andtime to first byte), for each client, and the model learns expectedscores for new clients.

The training may occur in near real-time to ensure that the contentfabric adapts fast to changes in resources, failed fabric nodes etc. Insome implementations, a computational matrix to continually train thissystem can scale with the number of clients and the number of pathsbetween fabric nodes. Modern TPU and parallel processing systems areoften equipped to perform fast matrix multiplication on very large scalematrices such as “billion×billion” dimensions and learning.

FIG. 12 shows an example of a continuous machine learning (ML) methodfor predicting best performing egress nodes and egress-origin segmentsper client. In FIG. 12, participating entities include a client 1204, anML system 1208 implemented on one or more fabric nodes of the contentfabric, an egress node 1212, and an origin node 1216. At stage 1220 ofFIG. 12, ML system 1208 is configured to geo-locate client 1204 todetermine a geographic region of the content fabric to serve client1204. In this example, at 1224, client 1204 calls a config( ) method atsystem 1208. At 1228, ML system 1208 performs the config( ) method togeo-locate client 1204, and the corresponding content fabric geographicregion is returned from system 1208 to client 1204 at 1232.

In the example of FIG. 12, current predicted scores for fabric nodes inthe determined geographic region for client 1204 are read at 1236 andare based on prior training. The prediction can be for a generic clientin situations where client 1204 is new to system 1208. In otherinstances, when client 1204 is a repeat, the prediction can be specificto client 1204. The current predicted scores are used by ML system 1208to identify a top scoring egress node for clients in the determinedcontent fabric region. In this example, the top scoring egress node isegress node 1212. Thus, at 1240, client 1204 issues a GET of a contentobject part to egress node 1212. If egress node 1212 finds at 1244 therequested content object part, at 1248, egress node 1212 returns thefound part back to client 1204. In instances where egress node 1212 isunable to find the requested content object part in memory or otherwiselocal to egress node 1212, at 1252, egress node 1212 finds among the topscoring egress-origin segments the origin nodes that contain therequested content object part by partition match and issues a GET usingthe intra-domain and inter-domain protocols described in greater detailherein to these origin nodes. In this example, origin node 1216completes a high scoring egress-origin segment for egress node 1212, andcontains the desired contain object part by partition match. Thus, at1256, egress node 1212 issues a GET to origin node 1216, origin node1216 identifies the content object part at 1258, and egress node 1212receives the desired content object part from origin node 1216. Thecontent object part is then returned from egress node 1212 back toclient 1204 at 1260.

In FIG. 12, in some implementations, at 1264, measurements can berecorded as content object parts are found and delivered to client 1204.In this example, when egress node 1212 finds the content object part onorigin node 1216, egress node 1212 can record measurements detailing theegress-origin segment, time to first byte (TTFB) to receive the contentobject part, and bandwidth as well as other parameters, as illustratedin FIG. 12. Also, in instances when egress node 1212 has the requestedcontent object part, such measurements can be recorded, in which case,TTFB=0, and bandwidth is not applicable (N/A). Also, as content objectparts are returned to client 1204, client-egress scores for TTFB andbandwidth per region can be aggregated at 1268 by ML system 1208. Insome implementations, such scores are retrieved from egress node 1212for aggregation 1272. In some implementations, egress-origin scores arealso aggregated for TTFB and bandwidth for all geographic regions at1276 of FIG. 12.

In the example of FIG. 12, at 1280, TTFB and bandwidth score predictionscan be retrained using these aggregated measurements. For instance, asillustrated by reference numeral 1284, ML system 1208 can retrain usinga collaborative filtering method using egress node measurements for eachgeographic region and egress-origin node measurements for all geographicregions in this example. Then, at 1288, new predicted scores can becomputed and stored in memory by system 1208. For instance, system 1208can call an updatescores( ) method 1292 to store new predicted scores.For example, for each geographic region, egress node scores can bestored, and for all geographic regions, egress-origin segment scores canbe stored.

In some implementations, in addition to selecting the best egress andcontent routes based on ML, popular content can be opportunisticallycached in the available capacity of the fabric nodes. In someimplementations, to select the best content to cache, a secondcontinuous learning model can be used to predict most popular content,and can be used in conjunction with JIT rendering of multiple versionsof output media through bitcode in the content fabric.

Predicting popular content can be considered a “time series” problem.For time series prediction, a gradient boosting decision tree is appliedin some implementations. In some other implementations, sequentialmodels may be used. In some implementations, the gradient boostingdecision tree is applied to a screening data set, where features includecontent request time, content request duration, content title, playoutplatform, and other content metadata.

In some implementations, there are two ways to split time series datainto training, validation, and testing: a side-by-side split and awalk-forward split. In the side-by-side split, which can be used for amainstream ML model, the data set is split into at least two portions,one used for training and the other used for testing (with the timeframeof both aligned). The walk-forward split, in comparison, is aimedspecifically at data sets with a strong correlation with time.

FIG. 13 shows an example of split schemes for training, validation, andtesting in a timeframe, where the timeframe of a data set chosen forvalidation is shifted forward by one prediction interval relative to thetimeframe for training. A walk-forward split 1304 and a side-by-sidesplit 1308 are shown. As shown in FIG. 13, train, validate and test canbe performed on the full data set but over different timeframes. Someimplementations use a walk-forward split approach, and experimentationcan be performed to determine the optimal time duration and time offsetin the model.

In some implementations, the model includes a number of features such ascontent categories, streaming type, request day of the week, and requestlifetime, as well as a number of statistical features derived from thedata set, such as time windows of popularity, global attentionstatistics and past request aggregation from previous time periods. Foraggregation methods, some implementations use mean, median, max, min,days since, and differences of mean values between adjacent timewindows.

In some implementations, the content fabric provides a universal (typeand size agnostic) distributed storage solution for digital content thatis fundamentally different than conventional distributed file systemsand cloud storage in a few key ways: 1) the content fabric avoidsduplication of storage or network bandwidth transmission as the contentis re-purposed for various output versions, 2) the content fabricprovides flexible personalization of the media delivered(programmability), and 3) the content fabric includes intrinsicversioning and an ability to prove the validity of a piece of contentand its version history.

In some implementations, a content object's data is stored in datacontainers called content object parts. A content object part is thefundamental unit of storage, network transfer and caching as mentionedabove. A content object part is immutable once finalized and identifiedby a hash value that is calculated across all of the content objectpart's content. Thanks to the use of a cryptographic hash function, theauthenticity of a content object part's data can be verified byrecalculating the hash. The hash also serves as criterion for datadeduplication.

In some implementations, when file data is ingested into the contentfabric, the file data is automatically partitioned into content objectparts to desirably have a consistent part size. Large file data can besplit up and stored in multiple content object parts. Multiple smallfiles can be aggregated into a single content object part. User-providedmetadata can also be stored in the content object parts, as describedabove with reference to FIG. 1. In some implementations, evenfabric-internal data structures such as a list of content object partsor content verification proofs can be stored in content object parts. Insome implementations, the content object is a small data structure thatreferences content object parts by hash and is stored itself as acontent object part.

FIG. 14 shows an example of a content object structure in the contentfabric. In this example, a content object includes structural portionsin the form of QREF 1404 and QSTRUCT 1408. The content object alsoincludes data portions in the form of metadata QMD 1412 and QMD2 1416,as well as opaque data in the form of opaque data parts 1420, 1424 and1428. For instance, one or more of opaque data parts 1420-1428 can be anopaque data blob such as raw data and/or code.

In FIG. 14, QREF 1404 of the content object is a content object hash.For instance, the QREF can be encoded in the Concise Binary ObjectRepresentation (CBOR) format. CBOR can be desirable in someimplementations because CBOR is JSON-like and schema-less, but moreefficient in size and faster in processing. The use of a standardencoding format can facilitate validation of the authenticity ofmetadata: the content object part hash allows validation of the contentobject part's binary content, while the open format allows extraction ofthe metadata from it.

In this example, QREF 1404 has sub-components including a QSTRUCT hash1404 a, a QMD hash 1404 b, a QMD2 hash 1404 c, and a content type hashlabeled QTYPEHASH 1404 d. In this example, QSTRUCT hash 1404 a is a hashof QSTRUCT 1408, which is also CBOR-encoded in this example. QSTRUCT1408 includes hashes of opaque data parts 1420, 1424 and 1428 as well asassociated proofs. In QREF 1404, QMD hash 1404 b is a hash of QMD 1412,which is CBOR-encoded structured data stored in an encryption keyspace 1. By the same token, QMD2 hash 1404 c of QREF 1404 is a hash ofQMD2 1416, which is also CBOR-encoded structured data and stored inencryption key space 2, as shown in FIG. 14. Opaque data parts 1420-1428can also be stored in designated encryption key spaces as illustrated inFIG. 14.

In some implementations, using a hierarchical reference structure,content objects scale from small to very large. In some implementations,none of the employed structures impose a limit on size, neither forbinary data nor for metadata. The reference structure also providesefficient and fast versioning of content. In some implementations,creating a new version of content includes copying the referencestructure, pointing back at the previous version's data, and thencreating new structures and data for the pieces that change in the newversion. For example, adding a new file to an existing content objectresults in a new (set of) data, a modified subset of metadata, andupdated internal structures. Existing file data (represented as existingcontent object parts) and the unchanged metadata subset are notduplicated in some implementations.

FIG. 15 shows an example of a method for finding content objects byhash. At 1504, a client 1506 obtains a content hash, QHASH, out of band,for example, linked on a website. At 1508, client 1506 finds an egressnode and requests to read content identified by QHASH. An example ofsuch an egress node is node B of FIG. 15. At 1512, node B parses QHASHand extracts a content object hash, QREF, in this example. At 1516, nodeB requests QREF from the content fabric, for instance, using a GET QREFcommand. In this example, node A of FIG. 15 has QREF and, thus,responds. Node B can then read contents of QREF and parse hashes QSTRUCTand QMD from QREF, as illustrated in FIG. 14. At 1520 of FIG. 15, node Bissues a GET QMD, and node C responds since QMD is stored on node C, asshown in FIG. 15. So node B can read contents of QMD. At 1524, node Brequests QSTRUCT and finds QSTRUCT on node F. When node B retrievesQSTRUCT from node F, at 1528, node B parses QSTRUCT to extract QPART 1and QPART 2. At 1532, node B then gets QPART 1 from node D and getsQPART 2 from node E, as shown in FIG. 15. At 1536, node B can returnQMD, QPART1, and QPART2 to client 1506.

FIG. 16 shows an example of a method for executing content programsagainst content object parts and metadata. In FIG. 16, client 1506performs operations 1504 and 1508 as described above with reference toFIG. 15. Node B performs operation 1512 as also described above. At 1616of FIG. 16, node B requests QREF from the content fabric using a GETQREF. In this example, node A responds, since node A has QREF. Node Bthen reads contents of QREF and extracts QTYPEHASH, referring to anothercontent object hash. At 1620, node B finds the other content object byhash and extracts a portion in that content object that contains code,QCODEPART. At 1624, node B continues to read content object parts andmetadata as described above at operations 1512-1532 of FIG. 15. Then,QCODEPART can be executed against the content object parts and metadataread at 1624. Results can then be returned to client 1506 at 1628.

FIG. 17 shows an example of content object versioning. In FIG. 17, afirst version 1704 of the content object structure of FIG. 14 is shown.A second version 1708 of the content object structure is also shown. Inthis example, a blockchain 1712 is also shown. In blockchain 1712 is acontent object contract at a first point in time 1716 and at a secondpoint in time 1720. In this example, at first time 1716, the contractidentifies QREF_v1, that is, version 1704. At the later time 1720, thecontent object contract identifies QREF_v1 as the previous version,while QREF_v2, i.e., second version 1708, is identified as the currentversion.

In the example of FIG. 17, at 1722, there is an instruction to updateversion 1704 by adding a new part, labeled part 4 in this example. Inresponse to this instruction, at 1724, a copy of the previous version ofQMD, QMD_v1, is made, and QMD_v1 is modified and saved as QMD_v2 insecond version 1708. At 1726, a copy of the first version of QSTRUCT,QSTRUCT_v1, is made, and a reference to part 4 is added to create asecond version of QSTRUCT, QSTRUCT_v2, also shown in second version1708. At 1728, a copy of QREF_v1 is made. References to QSTRUCT_v2 andQMD_v2 are stored and then saved as QREF_v2 in second version 1708. At1730, QREF_v2 is recorded as the current version to the content objectcontract of blockchain 1712.

FIG. 18 shows an example of a method for verification of metadata in acontent object. In FIG. 18, at 1804, a current version of QREF isprovided using a blockchain content object contract such as contract1720 of FIG. 17. At 1808 of FIG. 18, QREF contents are read, forinstance, in the form of hashes 1404 a-1404 d of FIG. 14. A checksum ofcanonical CBOR encoding of QREF is calculated at 1812 of FIG. 18. Thechecksum and QREF are compared at 1816. When QREF and the checksum arenot equal, verification of the metadata fails. At 1816, when QREF andchecksum are equal, verification is established. Returning to 1808,after reading the contents of QREF, at 1820, QMD hash 1404 b of FIG. 14is read. Metadata 1822 provided as an input in FIG. 18 is processed at1824, where a checksum of canonical CBOR encoding of the metadata iscalculated. This checksum is then compared at 1828 with the QMD hashread at 1820. When the QMD hash and checksum of 1824 are equal,verification is established. When the QMD hash and checksum of 1824 arenot equal, verification fails.

FIG. 19 shows an example of a method for verification of a full contentobject part. In FIG. 19, operations 1804, 1808, 1812 and 1816 asdescribed above with reference to FIG. 18 are performed. In FIG. 19,following 1808, QSTRUCT hash 1404 a of QREF 1404 of FIG. 14 is read at1904 of FIG. 19. Following 1904, at 1908, a checksum of canonical CBORencoding of QSTRUCT contents is calculated. This operation at 1908 alsofollows a determination at 1816 that operands are not equal. The QSTRUCThash and checksum of 1908 are then compared at 1912. If the QSTRUCT hashand checksum are not equal, verification fails. If the QSTRUCT hash andchecksum are equal, verification is established. Also, in FIG. 19, afull content object part is provided as an input at 1916. At 1920, achecksum of the content object part is calculated. This checksum iscompared with a QPARTHASH read from QSTRUCT 1408 of FIG. 14 at 1924 ofFIG. 19 in a compare operation at 1928. If the QPARTHASH and checksum of1920 are not equal, verification fails. If the QPARTHASH and checksum of1920 are equal, verification is established. It should also be notedthat operation 1924 is desirably repeated for all QPARTHASHes containedin QSTRUCT 1408 of FIG. 14 for separate verifications, in someimplementations.

FIG. 20 shows an example of a method for verification of a sub-portionof a content object part. In FIG. 20, operations 1804, 1808, 1812, 1816,1904, 1908 and 1912 are performed in the manner described above withreference to FIGS. 18 and 19. In FIG. 20, rather than a full contentobject part being provided as an input, a binary sub-portion of acontent object part is provided as an input at 2004. At 2008, a checksumof the sub-portion of the content object part is calculated. Then, at2012, a checksum of the root of a chunk Merkle tree based on thechecksum calculated at 2008 and based on proofs is calculated. At 2016,following 1904 in FIG. 20, QPARTHASH is read from QSTRUCT obtained at1904, and the Chunk Merkle Proof, as described in greater detail herein,based on QSTRUCT is calculated. Then, QPARTHASH is compared with theMerkle root checksum of 2012 at 2018. When QPARTHASH and the Merkle rootchecksum are not equal, verification fails. When QPARTHASH and theMerkle root checksum are equal, verification is established.

In some implementations, re-use of the same content in creation ofoutput variants, and consequent benefits to distribution efficiency andpersonalization, are facilitated in the content fabric through use ofJIT compilation capabilities that allow multiple front end compilers toleverage the optimization of compilation to machine code (backendcompilation) for multiple source code languages. In someimplementations, the system compiles the source code to an intermediaterepresentation language, e.g., abstract syntax tree (AST), and allowsfor development of maximally optimized compilation of AST to machinecode.

In some implementations, this method decouples “front end” compilationfrom source code, and “back end” compilation from source to machinecode, via AST, and many beneficial side effects, one of which is theability to support JIT compilation of source code to machine code viaAST. Also, the system may use cross-platform compilation (static anddynamic). In some implementations, the content fabric benefits byproviding a purpose-built “sandbox” for deploying code that modifiescontent objects. The sandbox can be part of the content fabric and canbe extended to clients using application programming interfaces (APIs)via web assembly machine code (WASM).

In some implementations, bitcode can be written in any supportedlanguage, e.g., C++, Go and Javascript for WASM, but different languagesmay be used. Some implementations define an interface between thecontent fabric and modules loaded JIT that allows for a content fabricmethod to call into the module, and the module to call back into thecontent fabric method. This calling context can facilitatereading/writing content and metadata to/from the content fabric, and canfacilitate a security sandbox for both authorizing code operations andmetering their use of system resources (e.g., for compensation andcharging).

FIG. 21 shows an example of a method for just-in-time (JIT) transcoding,packaging and transport within the content fabric. Entitiesparticipating in the method of FIG. 21 include client 2104, a fabricnode 2108, another fabric node having a desired content object part2112, and an origin fabric node 2116 in the case of a live streamingimplementation. At 2120 of FIG. 21, client 2014 requests to play digitalcontent in the form of a video. At 2124, client 2104 requests a segmentof the video from fabric node 2108. If fabric node 2108 finds at 2126the requested segment in cache at node 2108, the segment is returnedfrom node 2108 to client 2104 at 2128. In some instances, node 2108 isnot able to find the requested segment in node 2108's cache, so node2108 can determine at 2130 if a corresponding mezzanine-marked part, ahigher bit rate version of the segment, is stored in node 2108's cache.If the mezzanine version is found, the mezzanine version can betranscoded and returned, at 2132, to client 2104.

In FIG. 21, in some instances, if the requested segment and themezzanine version of the segment are not in cache at node 2108, node2108 can send a GET for the mezzanine version to the node having thepart 2112 at 2136, using routing methods described in greater detailherein. Node 2112 can return the mezzanine version to node 2108 at 2140.Node 2108 can then begin transcoding the received mezzanine version intoindividual segments 1, 2, 3, 4 . . . N at 2142. As the segments 1-N aretranscoded, node 2108 sends the transcoded segments to client 2104,where the segments can be desirably buffered ahead of playback at 2144.

In FIG. 21, in the case of live transmission, in some implementations,it is desirable to create and publish mezzanine versions of segmentsfrom origin fabric node 2116 at 2148 as those mezzanine versions areavailable in the live transmission. In this way, latency can beminimized or eliminated in streaming live video as the live video ispushed into a pre-established content object structure.

In some implementations, new output variants can be introduced withouthaving to create additional copies of a mezzanine source (languageversions, territory versions, repairs, new playout device formats, etc.)and can be updated or extended without changing or taking down thecontent fabric. In some implementations, bitcode stored in contentobject parts in the content fabric can be versioned and updated withouthaving to change other parts of the pipeline.

When new content is published to the content fabric, such as aninteroperable master format (IMF) package, an ingest content typetemplate can be selected by the user or client of the API. That contenttype implements bitcode that the content fabric invokes to write thecontent in the package to the content fabric. FIG. 22 shows an exampleof IMF package content in the content fabric after ingest of theselected content type, implemented with bitcode. In this example, forthe IMF content type, the audio and video tracks can be written ascontent object parts, and the relevant portions of the content playlists and offering playlists (CPLs and OPLs respectively) are written asmetadata key,value tuples. In some implementations, the core logic tocreate an output variant is written as a metadata value using a JSONobject; this object, termed an “offering,” is a grouping of the keynames for the audio and video tracks, starting time code, duration andparameters, for example, to create an output version. FIG. 23 shows anexample of description for an English language version of a consumerstreaming deliverable from an IMF package where the package specifiesmultiple language versions.

In some implementations, when a user requests to stream a DASH or HLSversion of a content object in its target language version, the bitcodemodule reads the appropriate metadata and the metadata pointing at theconstituent video and audio content object parts, reads the content ofthese parts, and generates a manifest file, which is then served to theclient and the stream, which is served. The manifest and the segmentscan be built on-the-fly by the bitcode. In this case, the bitcode drivesaudio/video processing modules to perform the scaling and bitratetranscoding to generate only the segments the client requests as therequests are made. For instance, in FIG. 21, there can be DASH and HLSmanifest files and segments generated from a mezzanine master package,for instance, in IMF format, using bitcode at the time of user request.

In some implementations, the bitcode environment in the content fabriccan accommodate code that operates on the parts such as raw data andmetadata of a content object in the content fabric for personalizing orcustomizing output such as consumable media. Some implementations usebitcode to, for example, apply custom watermarks, as described belowwith reference to FIG. 24, provide custom clipping functions, andimplement automatic video classification and metadata tagging via ML. Inthis case the ML model that provides the video classification code runsinside a container loaded by the bitcode sandbox, illustrating a breadthof possibilities for creating intelligent content coding pipelines. Insome implementations, the content fabric allows such operations to beembedded directly into the media delivery pipeline, to be versioned andupdated without affecting the rest of the pipeline, and to draw onre-usable metadata and content.

FIG. 24 shows an example of metadata stored in an original contentobject created from an IMF source package used to specify content,dimensions and placement of a watermark by bitcode in generating anoutput stream. In this example, the content object was created from anIMF source package with mezzanine level video.

In some implementations, the content fabric has capacity to storemetadata classifying the content, and, with the programmability of thebitcode sandbox, to use this metadata to create customized orpersonalized media output or to offer personalized, JIT searching andinterest matching of content in the fabric. A video classificationmachine using a deep learning pipeline can be incorporated, in someimplementations.

In some implementations, video tagging is performed to iterate videoframe by frame, e.g., using OpenCV, FFMPEG, or the content fabric AVpipe, followed by applying a per-frame encoding procedure using aconvolutional neural network, resulting in an n-dimensional vector perframe expressing the frame-level video features. The frame-levelfeatures are then aggregated to form a video-level feature vector, whichis then input to a video-level classifier to predict the video labels.

In some implementations of the content fabric video tagging pipeline,some videos are encoded into one-frame-per-second frames. Raw frames arefed into an inception network, and the ReLu activation of the lasthidden layer is fetched to form frame-level features. These frame-levelfeatures are aggregated. In some implementations, context gating, alearnable non-linear unit, is applied to the aggregated video-levelfeatures, followed by a video classifier—a mix of neural network expertsystems—to perform final tagging. Some implementations aggregate spatialrepresentation in recognition of ‘places.’

In some implementations of the video tagging architecture, contextgating is performed, which generally refers to training a non-lineargating unit such that relevant aspects of a video are enhanced, andoff-topic features are suppressed. Some implementations use a methodcalled “mix of neural network experts,” which is based on the originalmixture of expert methods in which one trains multiple simple ‘expert’networks, to optimize their ‘expert’ domain, followed by a convolutionwith a gating distribution, effectively learning both the parameters ofthe individual expert networks and the parameters of the gatingfunction. Such an approach effectively forms an ensemble for the finalprediction, where the bias and variance can be appropriately balanced,thereby overcoming overfitting effects in individual expert models.

The paradigm of engineering sufficiently “trustworthy” systems isbecoming increasingly difficult to sustain successfully as more and morecontent flows over the Internet to ever more variations of rightsmanagement, ever more points of vulnerability exist in the increasinglycomplex technological supply chain, and the value of digital contentincentivizes theft.

In some implementations, the content fabric backs content accesscontrol—operations to create, update, or access content—with blockchaintransactions executed through a native ledger embedded in a contentfabric software stack. The system ensures that parties are authentic,and its consensus ensures that only valid (authorized) transactions onthe content can be carried out. The content fabric can intrinsicallycouple control over the content's modification and access to theblockchain, while maintaining scalable storage and distribution outsideof the blockchain.

In some implementations, the content fabric allows content accesscontrol and authorization to take advantage of blockchain ledgers for“programmable” transactions between parties. For instance, eachtransaction on the blockchain can execute a small piece of code thatrepresents the terms of access for each content object. This small pieceof code is referred to as a smart contract. In some implementations, thecontent fabric may implement a blockchain that is compatible with theEthereum protocol and exposes an Ethereum Virtual Machine interface forapplications, although other protocols/blockchains may be used.

In some implementations, the ‘ledger’ is charged with at least threeoperations: 1) providing the authoritative ‘directory’ of contentincluding the only trusted reference to the list of versions of eachcontent object and the final verification ‘hash’ (the ground truth) foreach of these versions, 2) execution of the ‘access control’ logicallowing users to read and write content as well as contract termsenforcement and commercial terms reconciliation (payments and credits),and 3) recording access operations on content in the content fabric (the‘ledger’ operation).

In some implementations, the blockchain provides an ordered list oftransactions. Each transaction is performed by a blockchain participantand could have side effects: a state change in a particular account orcontract, a transfer of value, or one or more blockchain ‘events’.Transactions are identified by a ‘transaction ID.’ The content of atransaction as well as the ‘transaction receipt’ are available toblockchain participants. In some implementations, because the ledger ispublic, transactions can store ‘proofs’ of the activities they arerecording, for example in the content fabric, the final ‘checksum’ of anew content after an update. The way in which transactions can offerpublic verification of a particular action without revealing the detailsof the action belongs to a class of cryptography referred to aszero-knowledge proofs.

In some implementations, participants in the blockchain fall into twocategories: account owners and contracts. Account owners are primarilypeople in control of their actions against the blockchain, for example,creation or update of content, accessing or viewing content, etc.Applications operated by people or automated processes also can beaccount owners. These applications are often trusted by the people whorun them to do what they were constructed to do, and they are trusted tooperate the blockchain accounts they have been given access to. On theother hand, contracts are generally autonomous participants—they operatebased on their ‘code’. For example, a contract written to pay 2 creditsto each user who supplies a particular record signed by a signatureaccepted by this contract will behave the same way and pay the 2 creditswhen the signature is matched, and decline to pay otherwise. An accountowner generally is identified by its ‘address’ on the blockchain andowns a public/private key pair that it uses to sign its transactionsagainst the blockchain.

In some implementations, a contract is identified by its ‘address’. Thecontract will have an address if it has been successfully deployed byits creator. The creator is generally known because the creation of thecontract is done in a ‘transaction’ where the ‘from’ address is thecontract creator.

In some implementations, a content space is configurable to set basepolicies controlling access to associated content objects. The contentspace can be represented by the smart contract and, in such instances,is referred to as a content space contract. In some implementations, thecontent fabric operates as a single, global content space. In some otherimplementations, additional content spaces can be created for specialpurpose use.

In some implementations, a content node has a blockchain account,represented by its public/private key pair. By the same token, a user ofthe content fabric can have a blockchain account represented by itspublic/private key pair.

In some implementations, a library is implemented as a repository ofcontent, setting policies for how the library's content objects work.The library can be created inside a content space and can be representedby a smart contract, referred to herein as a library smart contract,which is determined by the containing content space. The library canhave a user as an owner.

In some implementations, content is a representation of a digital assetand is created in a library. The content can be represented by a smartcontract, referred to herein as a content smart contract, which isdetermined by the containing Library. In some implementations, eachcontent object has an instance of the content smart contract.

Users can have various roles, in some implementations. For instance, alibrary owner can dictate the behavior of content objects inside thelibrary, for example, who can create content, who owns the content oncecreated, how content is accessed or commercialized, etc. Another role ofa user is content owner. This owner of a content object can controlreading and writing access to the content. A content object can havemultiple owners, and these owners can have slightly differentprivileges, for example, modifying or updating the content object versuspublishing the content object for consumer access and determiningcommercial terms. In some implementations, a degree of control over thecontent is set by the library. Another role is consumer, referring to auser who does not own content and can access content based on thecontent object's contract terms, including commercial terms such asaccess or viewing charge.

FIG. 25 shows an example of a flow of content fabric operationsproviding content security and blockchain control. In someimplementations, operations on content objects in the content fabricfollow a flow of first invoking the appropriate blockchain transactionon the appropriate contract address, and then using the proof of a validtransaction—indicating that the operation is authorized—to make anauthorized API call on the content fabric. Specifically, a client 2404of the content fabric API securely obtains his/her public/private keys,for instance, by retrieving keys from a personal store or creating newkeys at 2408. At 2412, client 2404 creates a blockchain transactionsigned with the private key, and the transaction is recorded in ablockchain 2416 of content fabric 2420. At 2412, a specific contract andassociated method may be invoked. On successful completion of thetransaction (which can perform authorization logic), at 2424, client2404 creates an authorization token including the transaction ID, andpasses this token in a corresponding content fabric API 2428.Optionally, content fabric API 2428 may prompt client 2404 to call afinalization method on the contract in order to complete the APItransaction at 2432.

In some implementations, publishing a content object into the contentfabric assumes a content library has been created, and that libraryexists within a content space. The content library is created within thecontent space and, as such, often is based on the content space giving auser permission to do so. For example, a global content space can allowusers to create libraries for a fee. In some implementations, thecontent space is created by the originator of the content fabric, butadditional content spaces can also be created by participants in thecontent fabric for special use, such as for private or semi-privatesubsets of the content fabric with dedicated private fabric nodes.

In some implementations, because of their genesis roles, content spacesare trusted by fabric nodes, and fabric nodes are configured to trustcontent spaces by their maintainers. A new content space is created bydeploying the content space contract and configuring fabric nodes torecognize the new space.

In some implementations, to create a library, a user or client programmakes an API call directly, or via a user interface. The APIimplementation executes a method on the content space contract,createlibrary( ) which in turn will create a new instance of the librarysmart contract for that particular library based on the parametersspecified. The calling user becomes the owner of the library contractand as such will be able to further configure the library contract.

FIG. 26 shows an example of a method for implementing secure contentcreation in the content fabric. In some implementations, the creation ofa new content object for ingestion into the content fabric and updatingof existing content objects involves carrying out one or more of thefollowing operations, illustrated in FIG. 26, in which a client, “ALICE”2504 interacts with a blockchain 2508 and a fabric node 2512. At 2516,preparation begins by calling a method on the library contract to lookup possible content types and their security groups offered by thelibrary. At 2520, a method createcontent( ) is called on thecorresponding library contract, passing the content type and the chosensecurity groups. This returns a transaction ID and a new content ID. At2524, createcontent( ) is called on the content fabric passing in anauthorization token containing the transaction ID obtained above and thecontent ID, signed by the content creator. This returns from the contentfabric a valid write token. At 2528, a content encryption key set isgenerated: an AES symmetric key and a proxy re-encryption public,private key pair (AFGH) for each security group. Also at 2528, thecontent encryption key set is encrypted for three parties: the owner,the content fabric's key management service, and any owner delegates,using their respective public keys. Also at 2528, a methodsetsecuritymetadata( ) is called to store this data mapped to thesethree entities.

In FIG. 26, at 2540, content object parts are uploaded to the contentfabric, encrypting each content object part with the AES content keyfirst, and then using the AFGH key. At 2544, a method commit( ) iscalled on the library contract passing in the ‘content hash’, which is anew version of the content, uniquely identified by this hash. Note thateach content update API invocation returns the potential ‘content hash’if a version of the content were to be finalized without furthermodifications. At 2548, a finalize( ) method is called on the contentfabric passing an authorization token including the transaction IDobtained above and signed by the owner (the creator). At 2552, fabricnode 2512 supporting the operation calls a confirm( ) method on thelibrary contract passing in the final content hash, and signing thetransaction with the fabric node's key to prove that this fabric nodesupported the operation.

In some implementations, updating content in the content fabric skipsoperations 2516-2540 above and instead includes a call ofwriteaccessrequest( ) on the content contract to authorize the update,and then OpenWrite on the content fabric using the obtained transactionID in the authorization ID.

FIG. 27 shows an example of a method for implementing secure contentaccess in the content fabric. In some implementations, the consumptionof content as an output, e.g., consumable media, from the content fabricby a client involves carrying out one or more of the followingoperations. Entities participating in the method of FIG. 27 include aclient, “BOB” 2604, a blockchain 2608, a fabric node 2612, and a keymanagement service (KMS) 2616. At 2620, an ephemeral key set is created.At 2622, a proxy re-encryption public/private key (AFGH) is encryptedwith the public key of the consumer. At 2624, the ephemeral key set isencrypted for the content fabric's key management service using itspublic key. At 2628, a method accessrequest( ) is called on the contentobject's blockchain contract passing in the encrypted ephemeral key set.At 2632, the contract records the ephemeral key set in the contract'sstate using a unique key, escrows any value required by theAccessRequest from the consumer's credit, and returns the transactionID. At 2636, BOB 2604 calls a contentopen( ) method on the contentfabric passing in an authorization token containing the transaction IDobtained above, signed by the consumer. At 2640, fabric node 2612 callsKMS 2616 (the delegate) passing in the authorization token above,including the transaction ID and signed by the consumer for theauthorized accessrequest( ) At 2644, KMS 2616 verifies the request byverifying the transaction ID (consumer's signature and success status)and then generates: the proxy re-encryption key using the AFGH key inthe ephemeral key set, and returns the re-encryption key to the fabricnode; and an encrypted version of the content AES key, encrypted withthe consumer's public key.

In FIG. 27, at 2648, KMS 2616 calls accessgranted( ) on the contentcontract recording the re-encryption key and the content key encryptedfor the consumer, obtained above. The contract releases the “value” fromescrow to the content owner. At 2652, fabric node 2612 uses there-encryption key to re-encrypt the content from the original AFGH keyspace into the consumer's ephemeral AFGH key space. At 2656, BOB 2604reads the re-encryption content delivered by the fabric node and theencrypted key blob recorded by an accessgranted( ) contract method. BOB2604 is now able to decrypt the content as follows: extract the AEScontent decryption key from the encrypted key blob using its privatekey; first decrypt using its ephemeral AFGH secret key; and then decryptthe result using the AES content decryption key obtained above.Optionally, at 2660, BOB 2604 calls the content contract'saccesscomplete( ) method.

In some implementations, the re-encryption of content published by theowner for an authorized consumer occurs without the software stack orhost computing device it runs on having access to the plain text contentor to the AES content encryption key, allowing for a “trustless”re-encryption of the content. This capability utilizes proxyre-encryption, based on public/private key cryptography, allowing dataencrypted with one user's public key to be transformed such that it canbe decrypted with another user's private key. The re-encryptiontransformation is ‘permissioned’ in the sense that it is possible whenthe original, encrypting user generates a re-encryption key that isbased on the encrypting user's private key and the public key of thetarget user. The re-encryption key itself is protected and does notexpose useful information about the original encrypted data. Further,this re-encryption key is used by a third party—a proxy—to re-encryptthe data without it becoming unencrypted.

In some implementations, proxy re-encryption provides a useful andpowerful service for a secure (trustless) content management system toprevent unintentional or intentional unauthorized access to content;specifically, proxy re-encryption can allow for secure, encrypted datato be easily shared with other users without exposing valuable privatekeys with any intermediary technology or allowing a malicious end-userto trivially share keys that could be used to decrypt (and steal) othercontent in the system.

Unlike conventional content management systems, the disclosed contentfabric can be implemented to run in a distributed, trustlessenvironment. Security assumptions are different in a trustlessenvironment; in particular, it is not valid to entirely delegate contentsecurity to the content nodes themselves.

In some implementations, secure data is encrypted with a set of keysgenerated and stored by the content publisher. Two distinct sets of keysare generated for each content object part by default: a symmetric keyand a public/private key pair for proxy re-encryption. For instance, thesymmetric key and method can be AES-256. The content fabric's proxyre-encryption is implemented with pairing-based cryptography so apairing-friendly elliptic curve can be used. In some implementations,the content fabric uses the curve, BLS12-381.

In some implementations, while each form of cryptography independentlyprovides strong security guarantees, the two distinct key sets are usedto implement the trustless model. The symmetric content keys are managedsecurely in the content fabric. The keys for proxy re-encryption aremanaged by a separate, independent online system, e.g., a key managementservice. When a user is granted access to a content object, thesymmetric key is transmitted directly to the authorized user. This keyby itself can be insufficient to decrypt the data. To perform the proxyre-encryption, the authorized user's computing device generates its ownset of BLS12-381 keys. The system creates a re-encryption key based onthe original content owner's key and the content-specific key of the enduser. This key is then transmitted to one or more fabric nodes in thecontent fabric. These fabric nodes then proxy the encrypted data, usingthe provided re-encryption key to transform the data in real-time intothe target key of the end user. The end user then decrypts first withtheir private key and then the symmetric key. This form of two-tierencryption can ensure that the keys the end user controls cannot be usedto directly decrypt the original source data that is stored in thecontent fabric.

In some implementations, key generation, storage and management areperformed automatically and transparently on behalf of both the contentowner and content consumers. Encryption and decryption can be performedin near real-time while data is stored and retrieved from the contentfabric. A scalable library can be used for server-side processing, andthe same library is cross-compiled into Web Assembly (WASM) to executein client software, including modern browsers.

In some implementations, the content fabric's unique architecture andsecurity model allow for creating a blockchain-verifiable (tamperresistant) content versioning system that can both provide integrityverification of content and a traceable history of content versionchanges. Some implementations use fast proofs of content versionintegrity and the recording of content version history into theblockchain for traceability.

In some implementations, the content fabric security model provides forauthenticity of the parties and privacy of the content. With respect tointegrity of the content, the managing of content in the content fabrictakes into account that: 1) content can be created and updated by manyusers, 2) content can live in the content fabric for an extended time,and 3) content can be accessed by many users.

In some implementations, the content fabric uses a fast proof method, aChunk Merkle Proof, that allows a client reading a content object toverify the hashes of the object's constituent parts in a short time andtherefore allows a client to verify the integrity of a content objectwhich the client has read.

An example of the fast proof method is as follows:

Each content object part is broken down in smaller segments—forinstance, 1, 2 or 10-20 MB in size

Each segment is ‘hashed’ using, by default, SHA-256 (configurable toinclude future standards such as SHA-3)

Segment hashes are organized in a tree, such as a Merkle tree or aPatricia tree

This tree has the following properties:

The root of the Merkle is a hash that reflects each change in thecontent, so a given Merkle tree root fully identifies a particularversion of the content

For any given segment, some implementations can calculate what is calledthe Merkle proof that allows a user in possession of the segment toascertain that the segment is correct and it resolves to the known rootof the Merkle tree. This proof can be a list of hashes of adjacentbranches of the tree up to but not including the root hash.

Content object parts are listed in a special metadata store in a format,such as a CBOR format. The user can retrieve the data blob and verifythat (a) the root of the Merkle tree for the desired part is present and(b) the hash of the data blob is further resolved correctly toward thecontent version hash as described below.

Metadata stores are similarly stored in a data blob and can be verifiedthe same as #2 above.

The content object “reference store” can be a data blob containing thehash of the content object parts data blob #2 and metadata #3.

Using this structure, a user in possession of any part of a contentobject (e.g., data or metadata) can verify that this data is correct andresolves to the known hash of the content object version.

In some implementations, the ‘content object version’ is recorded in theblockchain upon creation of the content object and update of the contentobject. The content object version can be recorded in the Committransaction following a write to content. This transaction encodes theaddress of the account that performed the write operation and, incombination with the object proof, can prove unambiguously theblockchain account, and therefore the actor, responsible for a contentoperation. Applying this capability across the functioning of thecontent fabric allows for a transparent, provable chain of record forcontent as shown in FIG. 28. FIGS. 29 and 30 show examples of contentobject verification trees.

Another example of a content verification tree in JSON format is asfollows:

{ “hash”: “hq_—Qmemns VGtimQkKVCJRot79DAW5wazrWhYdB7x35Lxdx9Pq”, “qref”:{ “valid”: true, “hash”: “hqp_QmemnsVGtimQkKVCJRot79DAW5wazrWhYdB7x35Lxdx9Pq” }, “qmd”: { “valid”: true,“hash”: “hqp_QmdQXKXRLE4gzmphrbin6F51cESTKd6U5XLtwg7hi8UXsq”, “check”: {“valid”: true, “invalidValues”: [ ] } }, “qstruct”: { “valid”: true,“hash”: “hqp_QmdwfHGH9mX73ccw8NCQFvGh4i2oU1ChAVzdoiaw7BuojH”, “parts”: [{ “hash”: “hqp_QmUv75ETf9x22cjNNbTncVPMuKZpXLPHF7tVz2D9ACthqD”,“proofs”: { “rootHash”:“acda1564dfc542064b7becdbc2d107c84fe1f942337bfe3c06 f2b4eec5e806b8”,“chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 50, “finalized”: 116 },“size”: 2634 }, { “hash”: “hqp_QmUDs9mNqWLBuZcTEA7SUAJZb6SEqwoj18ETvKjwjXnePW”, “proofs”: { “rootHash”:“901d2cef310ba329a3fbfcadaaa776de13c1cab9cf342d88a7 9afb268ca5ec37”,“chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 51, “finalized”: 116 },“size”: 3270 }, { “hash”:“hqp_QmTy4yE4ti5xWjrMwhEvK29QYQ12ysoVdtSEp4L2qhmWaF”, “proofs”: {“rootHash”: “83a5f2b6b5ca0d4112862264dec3b778c1973fd442df1aaad5558b0415242289”, “chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 52,“finalized”: 116 }, “size”: 4740 }, { “hash”:“hqp_QmduUaq5891hFH7sBytrjXvwmw4EpGkRfpUZ8rdY6tFhum”, “proofs”: {“rootHash”: “9c50884174f78a6b13006f2fe0eb642b29cfd0813924e8da7c8bdd0abf735d6d”, “chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 52,“finalized”: 116 }, “size”: 4008 }, { “hash”:“hqp_QmSZhnv1qkQd3LdjEvHLWjP1dJbcBT45cdhiNfiv92UYPA”, “proofs”: {“rootHash”: “2baedb36c987a03c6d3afc4989de96aeb4303cc3b1ae750ca0e1ae1e80fa8c26”, “chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 51,“finalized”: 116 }, “size”: 3638 }, { “hash”:“hqp_QmcQC7wk72X3NmPSuoTm1Pi7anPZzrLaeXRrSouhY2onmV”, “proofs”: {“rootHash”: “ec776feaff71865a5bfcc512926a1ece766fd78339d2b5e69008dd8c31d2f99f”, “chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 51,“finalized”: 116 }, “size”: 3119 }, { “hash”:“hqp_QmV2pZmWfuqPtCCEjwPQPRAFQhJoP1DNEGHvP7Jft9xMma”, “proofs”: {“rootHash”: “c9a5b4e77c8a314bb31035aafd02c6a2c5adb19e1b673e64706ed7185200086c”, “chunkSize”: 49, “chunkNum”: 49, “chunkLen”:51, “finalized”: 116 }, “size”: 3725 }, { “hash”:“hqp_QmczFwuf1PmyZHbX1Tq4yQkUkohvPu2tQifgEMzE9TVCkp”, “proofs”: {“rootHash”: “9f336d22faec24919675fe38904e9bc317134dc913cda839d9e476f49ceb0a00”, “chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 52,“finalized”: 116 }, “size”: 4139 }, { “hash”:“hqp_QmQQSHdKE3u4PeNJ1idgoG3QRDEBEow ZNTaXmkwitQPVUv”, “proofs”: {“rootHash”: “f5a8225bc3df71e1b650ffa9b792ef712c1dde552cd984e5dc330201a65e8f3f”, “chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 51,“finalized”: 116 }, “size”: 3512 }, { “hash”:“hqp_QmXMd9EYhjkD8q3beCEpgCDbL4MnMXzHirhCiQwStEshQt”, “proofs”: {“rootHash”: “29e49c187271b344ba58154fd4ce6db181cdbfedfb87c6ab4ce548edcb942f76”, “chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 51,“finalized”: 116 }, “size”: 3406 }, { “hash”:“hqp_QmafFJSxwCf9HWNuc1iyzRfH8guNSLN4RUHsid8QXuHc4z”, “proofs”: {“rootHash”: “1fcae272731987195bf91c877733c5b80d276e56f091baef8b06af73e030d51b”, “chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 55,“finalized”: 116 }, “size”: 7407291 }, { “hash”:“hqp_QmaeAzsWkufMhbm5v8mSxwMJXJ3PmVynYbgYndTkPSdi6x”, “proofs”: {“rootHash”: “6bfc296a266462eb34c1fbad68d40a7f9805f0d4d6c2b75b0a5664fd5e844da2”, “chunkSize”: 49, “chunkNum”: 52, “chunkLen”: 51,“finalized”: 116 }, “size”: 38057642 }, { “hash”:“hqp_QmYNZvVwaSWAVKpRhst5quPRdBA1QnQks3EDx3H7B7PxEV”, “proofs”: {“rootHash”: “71ac3a124d0e17c38f269373c63321c53c0b4e19eda4863a6a2aebcc2eed06c1”, “chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 54,“finalized”: 116 }, “size”: 6726419 }, { “hash”:“hqp_QmbZtVndHd85atZL1y3PXp1idXa7RZJeQEeCT8NsqBYiKA”, “proofs”: {“rootHash”: “2d8d5f097e9aadf17219dd7f86e1ca8944aa5ef7b19710c7969a75bbd51965f3”, “chunkSize”: 49, “chunkNum”: 50, “chunkLen”: 49,“finalized”: 116 }, “size”: 15757990 }, { “hash”:“hqp_QmQXx83NoJnyuKp2SP8mTQh7LA9dsNNFS8q5bfSsC7PHeT”, “proofs”: {“rootHash”: “c85ea23550d2eb306ef5904f7480b6711e69fc47be07fd245b425472a447c74b”, “chunkSize”: 49, “chunkNum”: 50, “chunkLen”: 49,“finalized”: 116 }, “size”: 13814854 }, { “hash”:“hqp_QmRkG7AXbSc54KxzP13oW7226hEupTagggrN1w3sEjgSpE”, “proofs”: {“rootHash”: “665f3b9b11fd9ef2b334fc8ec28f9170708457a13cc693039fcd2c4b618b03c5”, “chunkSize”: 49, “chunkNum”: 49, “chunkLen”: 57,“finalized”: 116 }, “size”: 90509 } ] }, “valid”: true }

For instance, given a content object part with the root hash,“6bfc296a266462eb34c1fbad68d40a7f9805f0d4d6c2b75b0a5664fd5e844da2” and asegment size of 10 MB, the following proof is constructed such that if auser is in possession of any of the 10 MB segments, the hash tree can becalculated up to the ‘root_hash’.

{ “root_hash”:“6bfc296a266462eb34c1fbad68d40a7f9805f0d4d6c2b75b0a5664fd5e 844da2”,“proofs”: [ { “byte_beg”: 0, “byte_end”: 10485759, “proof”: [“6bfc296a266462eb34c1fbad68d40a7f9805f0d4d6c2b75b0a5664fd5e 844da2”,“33355cb42bb31bae5cbb1881afaa4b612b050654ec173b7a627489f844 dc8d26” ] },{ “byte_beg”: 10485760, “byte_end”: 20971519, “proof”: [“6bfc296a266462eb34c1fbad68d40a7f9805f0d4d6c2b75b0a5664fd5e 844da2”,“2bdff03703f7c71dd27e58ac52dc675a183b9df336ace85c928cb239b5 fe095c” ] },{ “byte_beg”: 20971520, “byte_end”: 31457279, “proof”: [“6bfc296a266462eb34c1fbad68d40a7f9805f0d4d6c2b75b0a5664fd5e 844da2”,“b0409db2ba129ba4279378e22bfebcc492e196544b9a09c9056aa9de 4927f07a” ] },{ “byte_beg”: 31457280, “byte_end”: 38057641, “proof”: [“6bfc296a266462eb34c1fbad68d40a7f9805f0d4d6c2b75b0a5664fd 5e844da2”,“0fd7816bd9f06f3cf976aa24034e5de704cae01eae1f1efa4e2013538f3e02 ea” ] }], “proof_data”: {“0fd7816bd9f06f3cf976aa24034e5de704cae01eae1f1efa4e2013538f3e02 ea” :“+FugPFTGTXwdYttZq5dn0MN7mxhQ326xWs46OfS9OQp0Jym4OAMAAAAAAAAAAADgAQAAAACptkQCAAAAALxUxk18HWLbWauXZ9DDe5sYUN9usVrOOjn0vTkKdCcp” ,“2bdff03703f7c71dd27e58ac52dc675a183b9df336ace85c928cb239b 5fe095c”:“+FugPedu9h8GXkgK0RrW0OD3SMiFqcLCElw7SYmLEEyOa1S4OAEAAAAAAAAAAACgAAAAAAD//z8BAAAAAG3nbvYfBl5ICtEa1tDg90jIhanCwhJcO0mJixBMjmtU” ,“33355cb42bb31bae5cbb1881afaa4b612b050654ec173b7a627489 f844dc8d26”:“+FugOgJLxu2N9IQYm7cXjVahA3nG+i5WKgjclAbdoPB4iw+4OAAAAAAAAAAAAAAAAAAAAAD//58AAAAAADoCS8btjfSEGJu3F41WoQN5xvouVioI3JQG3aDweIsP” ,“6bfc296a266462eb34c1fbad68d40a7f9805f0d4d6c2b75b0a566 4fd5e844da2”:“+JGAgICgMzVctCuzG65cuxiBr6pLYSsFBlTsFzt6YnSJ+ETcjSaAgKAr3/A3A/fHHdJ+WKxS3GdaGDud8zas6FySjLI5tf4JXICAgICgD9eBa9nwbzz5dqokA05d5wTK4B6uHx76TiATU48+AuqAgKCwQJ2yuhKbpCeTeOIr/rzEkuGWVEuaCckFaqneSSfweoCA”,“b0409db2ba129ba4279378e22bfebcc492e196544b9a09c9056aa 9de4927f07a”:“+FugNuieiyxh4owFusQG1Xj7t2Ogj4gGoC4NiRXqQLbjRuy4OAIAAAAAAAAAAABAAQAAAAD//98BAAAAAObonossYeKMBbrEBtV4+7djoI+IBqAuDYkV6kC240bs” } }

In some implementations, the content fabric provides for scaling andmonetizing the management and distribution of media withpersonalization, intelligence, and high efficiency. The followingdescribes several areas of applications. Other use cases are possible.

In some implementations, the content fabric's JIT distributioncapability allows consumer streaming variants differing in packagingformat (DASH, HLS), bitrate, scaling and platform (smart TV, mobile,desktop, cable/over the air set top) to be generated from single master,i.e., mezzanine level sources without pre-generation (transcoding,packaging) and storage of the variants, saving time to market,complexity costs, and eliminating significant use of storage anddistribution bandwidth.

In some implementations, failed content segments—bad or missing tracks,failed compliance, or other mistakes in quality control—can be correctedby replacing only the repaired portion of the master source, avoidingre-packaging and re-distributing new versions.

In some implementations, because the access to specific output variantsis authorized via the fabric smart contracts, specific territorialvariants and in theater versus home/retail release window policies(“avails terms”) can be encoded into smart contract policies, allowing asingle source to serve global, and time-varying availability contractsand reduce the huge multi-department upstream work to control andimplement date and time-specific availability terms.

In some implementations, flexibility and scalability of the smartcontract-controlled content access in the content fabric and theprogrammable content transformation via bitcode allows for powerful newmonetization opportunities directly between content owners, contentlicensees, content consumers and third party sponsors/advertisers. Thefollowing highlight a few categories:

In some implementations, users can earn credit via use of the custompre- and post-hooks in content AccessRequest methods, allowing thecontent fabric to credit a user for viewing an ad, and the credit can beapplied toward the subscription fee for accessing paid for content.Similarly users can choose to watch content with no ads. In someimplementations, the content fabric can provide that:

the advertiser can be assured that an entire ad was streamed and beprovided with details of the user that has watched the ad (optionallyreported through data passed into the smart contract method);

the user can precisely control and consent to the data that he or she iscontributing for collection, because the data collected can be verifiedthrough a blockchain transaction, and sponsor recipients can be traced;

ad coupling to content can be as “smart” as desired, based on matchingad content to user preferences or content tags (see next point) and canbe placed with the content via any dynamic insertion, overlay or evenin-content (scene based) product placement, using bitcode;

users may choose to watch content with NO ads, while the marketplacedynamics of the smart contracts allow the content owner to charge amarket-bearing price for the content subscription.

In some implementations, with tagging and in particular the automatedvideo classification and tagging of video via ML in the content fabric,and the flexibility of bitcode to “use” these tags as data to drivebitcode pipelines that generate the output content, advertising can bemade specific to the end user and, as described above, fitted with thecontent in highly integrated ways.

In some implementations, combining this programmability with smartcontracts, the content fabric can support giant-scale contentmarketplaces—where sponsors can bid on the content tags, owners canconnect with sponsors universally (without bespoke/pre-existingadvertising relationships), and users can even select content andadvertising of their specific interest through their indicatedpreferences. User access to advertising content can be precisely knowndue to the contract transactions around content delivery, and this proofand settlement between content owners and advertisers can be handledquickly via smart contracts. In some implementations, the content fabricconnects smart contracts to external digital payments services, sopayment can continue to be done in flat currency avoiding anyrequirement relying on or influenced by cryptocurrency.

In some implementations, bitcode can be used to allow users to view,select (“clip”) and download preferred content, such as in onlinearchives of news and sports, and the supporting smart contracts can feedback any metadata about the selected content, such as tags, or consenteduser data. Additionally, the price of content or even parts of thecontent can be intelligently updated by updating the content accesscharge in the smart contracts, allowing content owners to have smart,dynamic control to maximize the performance of their online contentplatforms.

Similarly, contributors of the content such as affiliate stations,partners, or content licensees can be directly paid based on thespecific content performance by crediting their accounts through a smartcontract transaction, avoiding delays, intermediate accounting andcreating an efficient incentive for performance.

In some implementations, the content fabric can increase the possibilityof direct performance-based payment through the following features:content licensees can accept contract terms quickly and digitallyimplementing a state recorded in a smart contract, and upload contentaccording to a template that is dynamically rendered from the contenttype, and automatically request approval from the content owner. Theapproval can automatically update the state of the content object'ssmart contract and in turn credit the account of the licensee.Similarly, audience performance data recorded into the content object'scontract can be used to compute and credit additional royalty payments.Both content owners and licensees benefit from the efficiency, scale,and performance incentives.

In some implementations, end users can be incentivized to review andreport quality problems in content with credit via the backing smartcontracts supplementing top-down content quality control with efficientcrowd-sourced efforts.

In some implementations, as described with respect to ML for videoclassification, the content fabric's scalable metadata storage withcontent allows for creating giant scale search engines in which userscan search for content matching preferences or even matching existingcontent samples. The “like this” content to be matched will itself betagged and content with “similar tags” located and scored.

In some implementations, the content fabric can be used to build newmedia networks, platforms and channels that are based on traceable,provable content. In some implementations, the content fabric makes itpossible to prevent this exploitation from the ground up by certifyingthe version and origin of any content it serves, and opens thepossibility for a new class of provable media platforms.

It should be noted that, despite references to particular computingparadigms and software tools herein, computing device programinstructions on which various implementations are based may correspondto any of a wide variety of programming languages, software tools anddata formats, and be stored in any type of non-transitorycomputer-readable storage media or memory device(s), and may be executedaccording to a variety of computing models including, for example, aclient/server model, a peer-to-peer model, on a stand-alone computingdevice, or according to a distributed computing model in which variousfunctionalities may be effected or employed at different locations. Inaddition, references to particular protocols herein are merely by way ofexample. Suitable alternatives known to those of skill in the art may beemployed.

Any of the computing devices described herein have components includingone or more processors, memory devices, input/output systems, etc.electrically coupled to each other, either directly or indirectly, andin communication with each other, either directly or indirectly, foroperative couplings. Such computing devices include clients as well asservers. For instance, computer code can be run using a processor in theform of a central processing unit such as an Intel processor or thelike. Data and code can be stored locally on the computing device oncomputer-readable media, examples of which are described in greaterdetail herein. In some alternatives, portions of data and code can bestored on other computing devices in a network. A computing device canbe implemented to have a processor system with a combination ofprocessors. An input system of the computing device may be anycombination of input devices, such as one or more keyboards, mice,trackballs, scanners, cameras, and/or interfaces to networks. An outputsystem of the computing device may be any combination of output devices,such as one or more monitors, printers, and/or interfaces to networks.

Any of the modules, models, engines and operations described herein maybe implemented at least in part as software code to be executed by aprocessor using any suitable computer language such as but not limitedto C, Go, Java, and C++, by way of example only. The software code maybe stored as a series of instructions or commands on a computer-readablemedium for storage and/or transmission. Suitable computer-readable mediainclude random access memory (RAM), read only memory (ROM), a magneticmedium such as a hard-drive or a floppy disk, an optical medium such asa compact disk (CD) or DVD (digital versatile disk), flash memory, andthe like. The computer-readable medium may be any combination of suchstorage or transmission devices. Computer-readable media encoded withthe software/program code may be packaged with a compatible computingdevice such as a client or a server as described above or providedseparately from other devices. Any such computer-readable medium mayreside on or within a single computing device or an entire computersystem, and may be among other computer-readable media within a systemor network. A computing device such as the clients described above mayinclude a monitor, printer, or other suitable display for providing anyof the results mentioned herein to a user.

While the subject matter of this application has been particularly shownand described with reference to specific implementations thereof, itwill be understood by those skilled in the art that changes in the formand details of the disclosed implementations may be made withoutdeparting from the spirit or scope of this disclosure. Examples of someof these implementations are illustrated in the accompanying drawings,and specific details are set forth in order to provide a thoroughunderstanding thereof. It should be noted that implementations may bepracticed without some or all of these specific details. In addition,well known features may not have been described in detail to promoteclarity. Finally, although various advantages have been discussed hereinwith reference to various implementations, it will be understood thatthe scope should not be limited by reference to such advantages. Rather,the scope should be determined with reference to the appended claims.

1. A decentralized system for just-in-time (JIT) processing and outputof digital content, the system comprising: a memory device; and one ormore processors in communication with the memory device, the one or moreprocessors configured to execute a software stack to provide a firstfabric node of a plurality of fabric nodes of an overlay networkimplemented in an application layer differentiated from an internetprotocol layer, the first fabric node configured to: obtain, from aclient, a client request for digital content from the overlay network,determine that the digital content is not stored locally at the firstfabric node, search the plurality of fabric nodes for a plurality ofsource content parts representing the digital content in the overlaynetwork, each source content part of the plurality of source contentparts comprising media, metadata and code, the searching of the fabricnodes comprising sending one or more network requests to at least asubset of the fabric nodes in communication with the first fabric node,obtain, from a second one or more of the subset of fabric nodes, theplurality of source content parts, process the plurality of sourcecontent parts to produce the digital content JIT, the processing of theplurality of source content parts comprising executing the code of eachsource content part of the plurality of source content parts, andprovide the digital content to the client.
 2. The system of claim 1, theplurality of source content parts defining a higher bit rate version ofthe digital content.
 3. (canceled)
 4. The system of claim 1, theprocessing of the plurality of source content parts comprising: reading,as input, the metadata and/or the media of each source content part. 5.The system of claim 1, wherein the code is source code stored in andread from one or more content object parts and compiled JIT.
 6. Thesystem of claim 1, wherein the requested digital content is personalizedto the client based on the plurality of source content parts.
 7. Thesystem of claim 1, wherein the provided digital content is consumablefor one or more of: rendering, playing, streaming, downloading, reading,viewing, packaging, copying or storing the digital content.
 8. Thesystem of claim 1, wherein the digital content comprises digital video.9. The system of claim 1, wherein the digital content comprises a livestream, and the plurality of source content parts were published to theoverlay network by an origin fabric node of the plurality of fabricnodes while ingressing a live source.
 10. The system of claim 1, whereinthe processing comprises transcoding associated with JIT compilation ofsource code to machine code.
 11. The system of claim 10, wherein the JITcompilation comprises: compiling the source code, stored in one or morecontent object parts, to an intermediate representation language, andcompiling the intermediate representation language to the machine code.12. The system of claim 10, wherein the JIT compilation comprisescross-platform compilation.
 13. A non-transitory computer-readablemedium storing program code to be executed by one or more processors,the program code comprising instructions configured to cause: providinga first fabric node of a plurality of fabric nodes of an overlay networkimplemented in an application layer differentiated from an internetprotocol layer, the first fabric node configured to: obtain, from aclient, a client request for digital content from the overlay network,determine that the digital content is not stored locally at the firstfabric node, search the plurality of fabric nodes for a plurality ofsource content parts representing the digital content in the overlaynetwork, each source content part of the plurality of source contentparts comprising media, metadata and code, the searching of the fabricnodes comprising sending one or more network requests to at least asubset of the fabric nodes in communication with the first fabric node,obtain, from a second one or more of the subset of fabric nodes, theplurality of source content parts, process the plurality of sourcecontent parts to produce the digital content JIT, the processing of theplurality of source content parts comprising executing the code of eachsource content part of the plurality of source content parts, andprovide the digital content to the client.
 14. The non-transitorycomputer-readable medium of claim 13, the plurality of source contentparts defining a higher bit rate version of the digital content. 15.(canceled)
 16. The non-transitory computer-readable medium of claim 13,the processing of the plurality of source content parts comprising:reading, as input, the metadata and/or the media of each source contentpart.
 17. The non-transitory computer-readable medium of claim 13,wherein the code is source code stored in and read from one or morecontent object parts and compiled JIT.
 18. The non-transitorycomputer-readable medium of claim 13, wherein the requested digitalcontent is personalized to the client based on the plurality of sourcecontent parts.
 19. The non-transitory computer-readable medium of claim13, wherein the provided digital content is consumable for one or moreof: rendering, playing, streaming, downloading, reading, viewing,packaging, copying or storing the digital content.
 20. Thenon-transitory computer-readable medium of claim 13, wherein the digitalcontent comprises a live stream, and the plurality of source contentparts were published to the overlay network by an origin fabric node ofthe plurality of fabric nodes while ingressing a live source.
 21. Thenon-transitory computer-readable medium of claim 13, wherein theprocessing comprises transcoding associated with JIT compilation ofsource code to machine code.
 22. A method comprising: obtaining, from aclient, at a first fabric node of a plurality of fabric nodes of anoverlay network implemented in an application layer differentiated froman internet protocol layer, a client request for digital content fromthe overlay network; determining that the digital content is not storedlocally at the first fabric node; searching the plurality of fabricnodes for a plurality of source content parts representing the digitalcontent in the overlay network, each source content part of theplurality of source content parts comprising media, metadata and code,the searching of the fabric nodes comprising sending one or more networkrequests to at least a subset of the fabric nodes in communication withthe first fabric node; obtaining, from a second one or more of thesubset of fabric nodes, the plurality of source content parts;processing the plurality of source content parts to produce the digitalcontent JIT, the processing of the plurality of source content partscomprising executing the code of each source content part of theplurality of source content parts; and providing the digital content tothe client.
 23. The method of claim 22, the plurality of source contentparts defining a higher bit rate version of the digital content. 24.(canceled)
 25. The method of claim 22, the processing of the pluralityof source content parts comprising: reading, as input, the metadataand/or the media of each source content part.
 26. The method of claim22, wherein the code is source code stored in and read from one or morecontent object parts and compiled JIT.
 27. The method of claim 22,wherein the requested digital content is personalized to the clientbased on the plurality of source content parts.
 28. The method of claim22, wherein the provided digital content is consumable for one or moreof: rendering, playing, streaming, downloading, reading, viewing,packaging, copying or storing the digital content.
 29. The method ofclaim 22, wherein the digital content comprises a live stream, and theplurality of source content parts were published to the overlay networkby an origin fabric node of the plurality of fabric nodes whileingressing a live source.
 30. The method of claim 22, wherein theprocessing comprises transcoding associated with JIT compilation ofsource code to machine code.
 31. The non-transitory computer-readablemedium of claim 21, wherein the JIT compilation comprises: compiling thesource code, stored in one or more content object parts, to anintermediate representation language, and compiling the intermediaterepresentation language to the machine code.
 32. The non-transitorycomputer-readable medium of claim 21, wherein the JIT compilationcomprises cross-platform compilation.
 33. The method of claim 30,wherein the JIT compilation comprises: compiling the source code, storedin one or more content object parts, to an intermediate representationlanguage, and compiling the intermediate representation language to themachine code.